Logo: to the web site of Uppsala University

uu.sePublications from Uppsala University
Change search
Refine search result
1 - 49 of 49
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Arnold, Hannah
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Panara, Virginia
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Palaeobiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Gorniok, Beata Filipek
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Skoczylas, Renae
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Ranefall, Petter
    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.
    Gloger, Marleen
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Hogan, Benjamin M.
    Koltowska, Katarzyna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Mafba and  Mafbb Differentially Regulate Lymphatic Endothelial Cell Migration in Topographically Distinct Manners2021In: SSRN Electronic Journal, E-ISSN 1556-5068Article in journal (Refereed)
    Abstract [en]

    Lymphangiogenesis is the formation of lymphatic vessels from pre-existing vessels, a dynamic process that requires cell migration. Regardless of location, lymphatic endothelial cell (LEC) progenitors probe their surroundings while migrating to form the lymphatic network. Lymphatic development regulation depends on the transcription factor MAFB in different species. Zebrafish Mafba, expressed in LEC progenitors, is essential for their migration in the trunk. However, the transcriptional mechanism that orchestrate LEC migration in different lymphatic endothelial beds remains elusive. Here, we uncover topographically different requirements of the two paralogues, Mafba and Mafbb, for lymphatic cell migration. Both mafba and mafbb are necessary for facial lymphatic development, but mafbb is dispensable for trunk lymphatic development. On the molecular level, we demonstrate a regulatory network where Vegfc-Vegfd-SoxF-Mafba-Mafbb are essential in the facial lymphangiogenesis. We identify that mafba and mafbb fine-tune the directionality of LEC migration and vessel morphogenesis that is ultimately necessary for lymphatic function. 

  • 2.
    Arnold, Hannah
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Panara, Virginia
    Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, Palaeobiology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Hussmann, Melina
    WWU Munster, Med Fac, Inst Cardiovasc Organogenesis & Regenerat, Munster, Germany..
    Gorniok, Beata Filipek
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Medical Biochemistry and Microbiology.
    Skoczylas, Renae
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Gloger, Marleen
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Vascular Biology.
    Allalou, Amin
    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.
    Hogan, Benjamin M.
    Univ Melbourne, Dept Anat & Physiol, Melbourne, Vic 3000, Australia.;Univ Melbourne, Sir Peter MacCallum Dept Oncol, Melbourne, Vic 3000, Australia.;Peter MacCallum Canc Ctr, Organogenesis & Canc Program, Melbourne, Vic 3000, Australia..
    Schulte-Merker, Stefan
    WWU Munster, Med Fac, Inst Cardiovasc Organogenesis & Regenerat, Munster, Germany..
    Koltowska, Katarzyna
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology.
    mafba and mafbb differentially regulate lymphatic endothelial cell migration in topographically distinct manners2022In: Cell Reports, E-ISSN 2211-1247, Vol. 39, no 12, article id 110982Article in journal (Refereed)
    Abstract [en]

    Lymphangiogenesis, formation of lymphatic vessels from pre-existing vessels, is a dynamic process that requires cell migration. Regardless of location, migrating lymphatic endothelial cell (LEC) progenitors probe their surroundings to form the lymphatic network. Lymphatic-development regulation requires the transcription factor MAFB in different species. Zebrafish Mafba, expressed in LEC progenitors, is essential for their migration in the trunk. However, the transcriptional mechanism that orchestrates LEC migration in different lymphatic endothelial beds remains elusive. Here, we uncover topographically different requirements of the two paralogs, Mafba and Mafbb, for LEC migration. Both mafba and mafbb are necessary for facial lymphatic development, but mafbb is dispensable for trunk lymphatic development. On the molecular level, we demonstrate a regulatory network where Vegfc-Vegfd-SoxF-Mafba-Mafbb is essential in facial lymphangiogenesis. We identify that mafba and mafbb tune the directionality of LEC migration and vessel morphogenesis that is ultimately necessary for lymphatic function.

    Download full text (pdf)
    fulltext
  • 3.
    Bengtsson, Ewert
    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.
    Ranefall, Petter
    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.
    Image analysis in digital pathology: Combining automated assessment of Ki67 staining quality with calculation of Ki67 cell proliferation index2019In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 95, no 7, p. 714-716Article in journal (Other academic)
  • 4.
    Bombrun, Maxime
    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.
    Gao, Hui
    Ranefall, Petter
    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.
    Mejhert, Niklas
    Arner, Peter
    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.
    Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models2017In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 91, no 11, p. 1068-1077Article in journal (Refereed)
  • 5.
    Bombrun, Maxime
    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.
    Ranefall, Petter
    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.
    Lindblad, Joakim
    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.
    Allalou, Amin
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Partel, Gabriele
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Solorzano, Leslie
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Qian, Xiaoyan
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Tomtebodavagen 23, S-17165 Solna, Sweden.
    Nilsson, Mats
    Stockholm Univ, Dept Biochem & Biophys, Sci Life Lab, Tomtebodavagen 23, S-17165 Solna, Sweden.
    Wählby, Carolina
    Uppsala University, Science for Life Laboratory, SciLifeLab.
    Decoding gene expression in 2D and 3D2017In: Image Analysis: Part II, Springer, 2017, p. 257-268Conference paper (Refereed)
    Abstract [en]

    Image-based sequencing of RNA molecules directly in tissue samples provides a unique way of relating spatially varying gene expression to tissue morphology. Despite the fact that tissue samples are typically cut in micrometer thin sections, modern molecular detection methods result in signals so densely packed that optical “slicing” by imaging at multiple focal planes becomes necessary to image all signals. Chromatic aberration, signal crosstalk and low signal to noise ratio further complicates the analysis of multiple sequences in parallel. Here a previous 2D analysis approach for image-based gene decoding was used to show how signal count as well as signal precision is increased when analyzing the data in 3D instead. We corrected the extracted signal measurements for signal crosstalk, and improved the results of both 2D and 3D analysis. We applied our methodologies on a tissue sample imaged in six fluorescent channels during five cycles and seven focal planes, resulting in 210 images. Our methods are able to detect more than 5000 signals representing 140 different expressed genes analyzed and decoded in parallel.

    Download full text (pdf)
    fulltext
  • 6.
    Bombrun, Maxime
    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.
    Ranefall, Petter
    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.
    A web application to analyse and visualize digital images at multiple resolutions2017Conference paper (Other academic)
    Abstract [en]

    Computerised image processing and automated quantification of cell and tissue morphology are becoming important tools for complementing visual assessment when investigating disease and/or drug response. The distribution and organisation of cells in intact tissue samples provides a rich visual-cognitive combination of information at multiple resolutions. The lowest magnification describes specific architectural patterns in the global tissue organization. At the same time, new methods for in situ sequencing of RNA allows profiling of gene expression at cellular resolution. Analysis at multiple resolutions thus opens up for large-scale comparison of genotype and phenotype. Expressed genes are locally amplified by molecular probes and rolling circle amplification, and decoded by repeating the sequencing cycle for the four letters of the genetic code. Using image processing methodologies on these giga-pixel images (40000 x 48000 pixels), we have identified more than 40 genes in parallel in the same tissue sample. Here, we present an open-source tool which combines the quantification of cell and tissue morphology with the analysis of gene expression. Our framework builds on CellProfiler, a free and open-source software developed for image based screening, and our viewing platform allow experts to visualize both gene expression patterns and quantitative measurements of tissue morphology with different overlays, such as the commonly used H&E staining. Furthermore, the user can draw regions of interest and extract local statistics on gene expression and tissue morphology over large slide scanner images at different resolutions. The TissueMaps platform provides a flexible solution to support the future development of histopathology, both as a diagnostic tool and as a research field.

  • 7.
    Bombrun, Maxime
    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.
    Ranefall, Petter
    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.
    TissueMaps: A large multi-scale data analysis platform for digital image application built on open-source software2016Conference paper (Other academic)
    Abstract [en]

    Automated analysis of microscopy data and quantification of cell and tissue morphology has become an important tool for investigating disease and/or drug response. New methods of in situ sequencing of RNA allows profiling of gene expression at cellular resolution in intact tissue samples, and thus opens up for large-scale comparison of genotype and phenotype. Expressed genes are locally amplified by molecular probes and rolling circle amplification, and decoded by analysis of repeated imaging and sequencing cycles. Using image processing methodologies on these giga-pixel images (40000 x 48000 pixels), we have identified more than 40 genes in parallel in the same tissue sample. On the other hand, the distribution and organisation of cells in the tissue contain rich information at multiple resolutions. The lowest resolution describes the global tissue arrangement, while the cellular resolution allows us to quantify gene expression and morphology of individual cells.

    Here, we present an open-source tool which combine the analysis of gene expression with quantification of cell and tissue morphology. Our framework builds on CellProfiler, a free and open-source software developed for image based screening, and our viewing platform allow experts to visualize analysis results with different overlays, such as the commonly used H&E staining. Furthermore, the user can draw regions of interest and extract local statistics on gene expression and tissue morphology over large slide scanner images at different resolutions (Fig.1). The TissueMaps platform provides a flexible solution to support the future development of histopathology, both as a diagnostic tool and as a research field.

  • 8.
    Clausson, Carl-Magnus
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Arngården, Linda
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Ishaq, Omer
    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.
    Klaesson, Axel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Kühnemund, Malte
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Grannas, Karin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Koos, Björn
    Qian, Xiaoyan
    Ranefall, Petter
    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.
    Krzywkowski, Tomasz
    Brismar, Hjalmar
    Nilsson, Mats
    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.
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Immunology, Genetics and Pathology, Molecular tools.
    Compaction of rolling circle amplification products increases signal integrity and signal–to–noise ratio2015In: Scientific Reports, E-ISSN 2045-2322, Vol. 5, p. 12317:1-10, article id 12317Article in journal (Refereed)
  • 9. Gibbs, Anna
    et al.
    Buggert, Marcus
    Edfeldt, Gabriella
    Ranefall, Petter
    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.
    Introini, Andrea
    Cheuk, Stanley
    Martini, Elisa
    Eidsmo, Liv
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Broliden, Kristina
    Tjernlund, Annelie
    Human Immunodeficiency Virus-Infected Women Have High Numbers of CD103-CD8+ T Cells Residing Close to the Basal Membrane of the Ectocervical Epithelium2018In: Journal of Infectious Diseases, ISSN 0022-1899, E-ISSN 1537-6613, Vol. 218, no 3, p. 453-465Article in journal (Refereed)
  • 10. Gibbs, Anna
    et al.
    Buggert, Marcus
    Edfeldt, Gabriella
    Ranefall, Petter
    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.
    Introini, Andrea
    Cheuk, Stanley
    Martini, Elisa
    Eidsmo, Liv
    Hirbod, Taha
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    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.
    Broliden, Kristina
    Tjernlund, Annelie
    Increased numbers of CD103-CD8+ TRM cells in the cervical mucosa of HIV-infected women2017In: Scandinavian Journal of Immunology, ISSN 0300-9475, E-ISSN 1365-3083, Vol. 86, no 4, p. 288-289Article in journal (Other academic)
  • 11. Gibbs, Anna
    et al.
    Buggert, Marcus
    Ranefall, Petter
    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.
    Introini, Andrea
    Cheuk, Stanley
    Eidsmo, Liv
    Hirbod, Taha
    Ball, Terry B.
    Kimani, Joshua
    Kaul, Rupert
    Karlsson, Annika C.
    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. Uppsala University, Science for Life Laboratory, SciLifeLab.
    Broliden, Kristina
    Tjernlund, Annelie
    Analysis of the distribution of CD103 on CD8 T cells in blood and genital mucosa of HIV-infected female sex workers2016In: AIDS Research and Human Retroviruses, ISSN 0889-2229, E-ISSN 1931-8405, Vol. 32, no S1, p. 307-307Article in journal (Refereed)
  • 12.
    Hallberg, Ida
    et al.
    Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Dept Clin Sci, Div Reprod, SE-75007 Uppsala, Sweden..
    Persson, Sara
    Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Dept Clin Sci, Div Reprod, SE-75007 Uppsala, Sweden.;Swedish Museum Nat Hist, Environm Res & Monitoring, POB 50007, SE-10405 Stockholm, Sweden..
    Olovsson, Matts
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Women's and Children's Health, Reproductive biology. Ctr Reprod Biol Uppsala, SE-75237 Uppsala, Sweden..
    Moberg, Mikaela
    Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Dept Clin Sci, Div Reprod, SE-75007 Uppsala, Sweden..
    Ranefall, Petter
    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.
    Laskowski, Denise
    Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Dept Clin Sci, Div Reprod, SE-75007 Uppsala, Sweden..
    Damdimopoulou, Pauliina
    Karolinska Inst, Dept Clin Sci Intervent & Technol, Div Obstet & Gynecol, SE-14186 Stockholm, Sweden.;Karolinska Univ Hosp, SE-14186 Stockholm, Sweden..
    Sirard, Marc-Andre
    Laval Univ, Dept Anim Sci, Quebec City, PQ G1V 0A6, Canada..
    Rüegg, Joëlle
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Environmental toxicology. Ctr Reprod Biol Uppsala, SE-75236 Uppsala, Sweden..
    Sjunnesson, Ylva C. B.
    Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Dept Clin Sci, Div Reprod, SE-75007 Uppsala, Sweden..
    Bovine oocyte exposure to perfluorohexane sulfonate (PFHxS) induces phenotypic, transcriptomic, and DNA methylation changes in resulting embryos in vitro2022In: Reproductive Toxicology, ISSN 0890-6238, E-ISSN 1873-1708, Vol. 109, p. 19-30Article in journal (Refereed)
    Abstract [en]

    Knowledge on the effects of perfluorohexane sulfonate (PFHxS) on ovarian function is limited. In the current study, we investigated the sensitivity of oocytes to PFHxS during in vitro maturation (IVM), including conse-quences on embryo development at the morphological, transcriptomic, and epigenomic levels. Bovine cumulus-oocyte complexes (COCs) were exposed to PFHxS during 22 h IVM. Following fertilisation, developmental competence was recorded until day 8 of culture. Two experiments were conducted: 1) exposure of COCs to 0.01 mu g mL(-1) -100 mu g mL(-1) PFHxS followed by confocal imaging to detect neutral lipids and nuclei, and 2) exposure of COCs to 0.1 mu g mL(-1) PFHxS followed by analysis of transcriptomic and DNA methylation changes in blastocysts. Decreased oocyte developmental competence was observed upon exposure to & nbsp;>= 40 mu g mL(-1) PFHxS and altered lipid distribution was observed in the blastocysts upon exposure to 1-10 mu g mL(-1) PFHxS (not observed at lower or higher concentrations). Transcriptomic data showed that genes affected by 0.1 mu g mL(-1) PFHxS were enriched for pathways related to increased synthesis and production of reactive oxygen species. Enrichment for peroxisome proliferator-activated receptor-gamma and oestrogen pathways was also observed. Genes linked to DNA methylation changes were enriched for similar pathways. In conclusion, exposure of the bovine oocyte to PFHxS during the narrow window of IVM affected subsequent embryonic development, as reflected by morphological and mo- lecular changes. This suggests that PFHxS interferes with the final nuclear and cytoplasmic maturation of the oocyte leading to decreased developmental competence to blastocyst stage.

    Download full text (pdf)
    fulltext
  • 13.
    Hallström, Erik
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology.
    Kandavalli, Vinodh
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Sysmex Astrego AB, Uppsala, Sweden..
    Elf, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology.
    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.
    Label-free deep learning-based species classification of bacteria imaged by phase-contrast microscopy2023In: PloS Computational Biology, ISSN 1553-734X, E-ISSN 1553-7358, Vol. 19, no 11, article id e1011181Article in journal (Refereed)
    Abstract [en]

    Reliable detection and classification of bacteria and other pathogens in the human body, animals, food, and water is crucial for improving and safeguarding public health. For instance, identifying the species and its antibiotic susceptibility is vital for effective bacterial infection treatment. Here we show that phase contrast time-lapse microscopy combined with deep learning is sufficient to classify four species of bacteria relevant to human health. The classification is performed on living bacteria and does not require fixation or staining, meaning that the bacterial species can be determined as the bacteria reproduce in a microfluidic device, enabling parallel determination of susceptibility to antibiotics. We assess the performance of convolutional neural networks and vision transformers, where the best model attained a class-average accuracy exceeding 98%. Our successful proof-of-principle results suggest that the methods should be challenged with data covering more species and clinically relevant isolates for future clinical use. Bacterial infections are a leading cause of premature death worldwide, and growing antibiotic resistance is making treatment increasingly challenging. To effectively treat a patient with a bacterial infection, it is essential to quickly detect and identify the bacterial species and determine its susceptibility to different antibiotics. Prompt and effective treatment is crucial for the patient's survival. A microfluidic device functions as a miniature "lab-on-chip" for manipulating and analyzing tiny amounts of fluids, such as blood or urine samples from patients. Microfluidic chips with chambers and channels have been designed for quickly testing bacterial susceptibility to different antibiotics by analyzing bacterial growth. Identifying bacterial species has previously relied on killing the bacteria and applying species-specific fluorescent probes. The purpose of the herein proposed species identification is to speed up decisions on treatment options by already in the first few imaging frames getting an idea of the bacterial species, without interfering with the ongoing antibiotics susceptibility testing. We introduce deep learning models as a fast and cost-effective method for identifying bacteria species. We envision this method being employed concurrently with antibiotic susceptibility tests in future applications, significantly enhancing bacterial infection treatments.

    Download full text (pdf)
    FULLTEXT01
  • 14.
    Hellström, M
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, K
    Brändstedt, S
    Busch, Christer
    Effect of androgen deprivation on epithelial and mesenchymal tissue components in localized prostate cancer1997In: British Journal of Urology, ISSN 0007-1331, E-ISSN 1365-2176, Vol. 79, no 3, p. 421-426Article in journal (Refereed)
    Abstract [en]

    Objective To measure the area distribution of epithelial and mesenchymal components in the prostate of patients with localized prostate cancer after temporary androgen deprivation. Patients and methods Surgical specimens from 38 patients treated with the

  • 15.
    Kecheril Sadanandan, Sajith
    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.
    Baltekin, Özden
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Magnusson, Klas E. G.
    Boucharin, Alexis
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    Ranefall, Petter
    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.
    Jaldén, Joakim
    Elf, Johan
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Cell and Molecular Biology, Computational and Systems Biology.
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Segmentation and track-analysis in time-lapse imaging of bacteria2016In: IEEE Journal on Selected Topics in Signal Processing, ISSN 1932-4553, E-ISSN 1941-0484, Vol. 10, no 1, p. 174-184Article in journal (Refereed)
    Abstract [en]

    In this paper, we have developed tools to analyze prokaryotic cells growing in monolayers in a microfluidic device. Individual bacterial cells are identified using a novel curvature based approach and tracked over time for several generations. The resulting tracks are thereafter assessed and filtered based on track quality for subsequent analysis of bacterial growth rates. The proposed method performs comparable to the state-of-the-art methods for segmenting phase contrast and fluorescent images, and we show a 10-fold increase in analysis speed.

  • 16.
    Kecheril Sadanandan, Sajith
    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.
    Ranefall, Petter
    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.
    Le Guyader, Sylvie
    Center for Biosciences, Department of Biosciences and Nutrition, Novum, Karolinska Institutet, Huddinge, Sweden.
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automated training of deep convolutional neural networks for cell segmentation2017In: Scientific Reports, E-ISSN 2045-2322, Vol. 7, article id 7860Article in journal (Refereed)
    Abstract [en]

    Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.

    Download full text (pdf)
    fulltext
  • 17.
    Kecheril Sadanandan, Sajith
    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.
    Ranefall, Petter
    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. 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, 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.
    Feature augmented deep neural networks for segmentation of cells2016In: Computer Vision – ECCV 2016 Workshops: Part I, Springer, 2016, p. 231-243Conference paper (Refereed)
  • 18.
    Leclercq, Anna
    et al.
    Swedish Univ Agr Sci, Div Reprod, Dept Clin Sci, Uppsala, Sweden.;Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Uppsala, Sweden..
    Ranefall, Petter
    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.
    Sjunnesson, Ylva Cecilia Björnsdotter
    Swedish Univ Agr Sci, Div Reprod, Dept Clin Sci, Uppsala, Sweden.;Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Uppsala, Sweden..
    Hallberg, Ida
    Swedish Univ Agr Sci, Div Reprod, Dept Clin Sci, Uppsala, Sweden.;Swedish Univ Agr Sci, Ctr Reprod Biol Uppsala, Uppsala, Sweden..
    Occurrence of late-apoptotic symptoms in porcine preimplantation embryos upon exposure of oocytes to perfluoroalkyl substances (PFASs) under in vitro meiotic maturation2022In: PLOS ONE, E-ISSN 1932-6203, Vol. 17, no 12, article id e0279551Article in journal (Refereed)
    Abstract [en]

    The objectives of this study were to evaluate the effect of perfluoroalkyl substances on early embryonic development and apoptosis in blastocysts using a porcine in vitro model. Porcine oocytes (N = 855) collected from abattoir ovaries were subjected to perfluorooctane sulfonic acid (PFOS) (0.1 mu g/ml) and perfluorohexane sulfonic acid (PFHxS) (40 mu g/ml) during in vitro maturation (IVM) for 45 h. The gametes were then fertilized and cultured in vitro, and developmental parameters were recorded. After 6 days of culture, resulting blastocysts (N = 146) were stained using a terminal deoxynucleotidyl transferase dUTP nick end labeling (TUNEL) assay and imaged as stacks using confocal laser scanning microscopy. Proportion of apoptotic cells as well as total numbers of nuclei in each blastocyst were analyzed using objective image analysis. The experiment was run in 9 replicates, always with a control present. Effects on developmental parameters were analyzed using logistic regression, and effects on apoptosis and total numbers of nuclei were analyzed using linear regression. Higher cell count was associated with lower proportion of apoptotic cells, i.e., larger blastocysts contained less apoptotic cells. Upon PFAS exposure during IVM, PFHxS tended to result in higher blastocyst rates on day 5 post fertilization (p = 0.07) and on day 6 post fertilization (p = 0.05) as well as in higher apoptosis rates in blastocysts (p = 0.06). PFHxS resulted in higher total cell counts in blastocysts (p = 0.002). No effects attributable to the concentration of PFOS used here was seen. These findings add to the evidence that some perfluoroalkyl substances may affect female reproduction. More studies are needed to better understand potential implications for continued development as well as for human health.

    Download full text (pdf)
    FULLTEXT01
  • 19.
    Matuszewski, Damian J.
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Learning Cell Nuclei Segmentation Using Labels Generated with Classical Image Analysis Methods2021In: Proceedings of the WSCG 2021 / [ed] Vaclav Skala, University of West Bohemia , 2021, Vol. CSRN 3101, p. 335-338Conference paper (Refereed)
    Abstract [en]

    Creating manual annotations in a large number of images is a tedious bottleneck that limits deep learning use inmany applications. Here, we present a study in which we used the output of a classical image analysis pipeline aslabels when training a convolutional neural network (CNN). This may not only reduce the time experts spendannotating images but it may also lead to an improvement of results when compared to the output from the classicalpipeline used in training. In our application, i.e., cell nuclei segmentation, we generated the annotations usingCellProfiler (a tool for developing classical image analysis pipelines for biomedical applications) and trained onthem a U-Net-based CNN model. The best model achieved a 0.96 dice-coefficient of the segmented Nuclei and a0.84 object-wise Jaccard index which was better than the classical method used for generating the annotations by0.02 and 0.34, respectively. Our experimental results show that in this application, not only such training is feasiblebut also that the deep learning segmentations are a clear improvement compared to the output from the classicalpipeline used for generating the annotations.

  • 20.
    Ossinger, Alexander
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Bajic, Andrej
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Pan, S
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Andersson, Brittmarie
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Ranefall, Petter
    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.
    Hailer, Nils P.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    Schizas, Nikos
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Orthopaedics.
    A rapid and accurate method to quantify neurite outgrowth from cell and tissue cultures: Two image analytic approaches using adaptive thresholds or machine learning2020In: Journal of Neuroscience Methods, ISSN 0165-0270, E-ISSN 1872-678X, Vol. 331, article id 108522Article in journal (Refereed)
    Abstract [en]

    BACKGROUND: Assessments of axonal outgrowth and dendritic development are essential readouts in many in vitro models in the field of neuroscience. Available analysis software is based on the assessment of fixed immunolabelled tissue samples, making it impossible to follow the dynamic development of neurite outgrowth. Thus, automated algorithms that efficiently analyse brightfield images, such as those obtained during time-lapse microscopy, are needed.

    NEW METHOD: We developed and validated algorithms to quantitatively assess neurite outgrowth from living and unstained spinal cord slice cultures (SCSCs) and dorsal root ganglion cultures (DRGCs) based on an adaptive thresholding approach called NeuriteSegmantation. We used a machine learning approach to evaluate dendritic development from dissociate neuron cultures.

    RESULTS: NeuriteSegmentation successfully recognized axons in brightfield images of SCSCs and DRGCs. The temporal pattern of axonal growth was successfully assessed. In dissociate neuron cultures the total number of cells and their outgrowth of dendrites were successfully assessed using machine learning.

    COMPARISON WITH EXISTING METHODS: The methods were positively correlated and were more time-saving than manual counts, having performing times varying from 0.5-2 minutes. In addition, NeuriteSegmentation was compared to NeuriteJ®, that uses global thresholding, being more reliable in recognizing axons in areas of intense background.

    CONCLUSION: The developed image analysis methods were more time-saving and user-independent than established approaches. Moreover, by using adaptive thresholding, we could assess images with large variations in background intensity. These tools may prove valuable in the quantitative analysis of axonal and dendritic outgrowth from numerous in vitro models used in neuroscience.

  • 21.
    ovrebo, Oystein
    et al.
    Imperial Coll London, Dept Mat, London SW7 2AZ, England.;Imperial Coll London, Dept Bioengn, London SW7 2AZ, England.;Imperial Coll London, Inst Biomed Engn, London SW7 2AZ, England..
    Ojansivu, Miina
    Karolinska Inst, Dept Med Biochem & Biophys, S-17177 Stockholm, Sweden..
    Kartasalo, Kimmo
    Karolinska Inst, Dept Med Epidemiol & Biostat, S-17177 Stockholm, Sweden..
    Barriga, Hanna M. G.
    Karolinska Inst, Dept Med Biochem & Biophys, S-17177 Stockholm, Sweden..
    Ranefall, Petter
    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.
    Holme, Margaret N.
    Karolinska Inst, Dept Med Biochem & Biophys, S-17177 Stockholm, Sweden..
    Stevens, Molly M.
    Imperial Coll London, Dept Mat, London SW7 2AZ, England.;Imperial Coll London, Dept Bioengn, London SW7 2AZ, England.;Imperial Coll London, Inst Biomed Engn, London SW7 2AZ, England.;Karolinska Inst, Dept Med Biochem & Biophys, S-17177 Stockholm, Sweden..
    RegiSTORM: channel registration for multi-color stochastic optical reconstruction microscopy2023In: BMC Bioinformatics, E-ISSN 1471-2105, Vol. 24, no 1, article id 237Article in journal (Refereed)
    Abstract [en]

    Background: Stochastic optical reconstruction microscopy (STORM), a super-resolution microscopy technique based on single-molecule localizations, has become popular to characterize sub-diffraction limit targets. However, due to lengthy image acquisition, STORM recordings are prone to sample drift. Existing cross-correlation or fiducial marker-based algorithms allow correcting the drift within each channel, but misalignment between channels remains due to interchannel drift accumulating during sequential channel acquisition. This is a major drawback in multi-color STORM, a technique of utmost importance for the characterization of various biological interactions.

    Results: We developed RegiSTORM, a software for reducing channel misalignment by accurately registering STORM channels utilizing fiducial markers in the sample. RegiSTORM identifies fiducials from the STORM localization data based on their non-blinking nature and uses them as landmarks for channel registration. We first demonstrated accurate registration on recordings of fiducials only, as evidenced by significantly reduced target registration error with all the tested channel combinations. Next, we validated the performance in a more practically relevant setup on cells multi-stained for tubulin. Finally, we showed that RegiSTORM successfully registers two-color STORM recordings of cargo-loaded lipid nanoparticles without fiducials, demonstrating the broader applicability of this software.

    Conclusions: The developed RegiSTORM software was demonstrated to be able to accurately register multiple STORM channels and is freely available as open-source (MIT license) at https://github.com/oystein676/RegiSTORM.git and https://doi.org/10.5281/ zenodo.5509861 (archived), and runs as a standalone executable (Windows) or via Python (Mac OS, Linux).

    Download full text (pdf)
    FULLTEXT01
  • 22. Ranefall, P
    et al.
    Egevad, L
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Nordin, B
    Busch, C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bengtsson, E
    A new method for segmentation of colour images applied on immunohistochemically stained cells.1997In: Anal Cell Pathol, Vol. 15, p. 145-Article in journal (Refereed)
  • 23.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Towards Automatic Quantification of Immunohistochemistry Using Colour Image Analysis1998Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Quantification of the proportions of specifically stained regions in images is of significant interest in a growing number of biomedical applications. These applications includes histology and cytology where quantification of various stainings performed on histological tissue sections, smears, imprints etc. is of utmost importance. Through the use of special stains biological components of interest can be given a specific colour. Qualitatively this can be evaluated visually as the presence of a specific colour. But to perform a quantitative evaluation the number of stained cell nuclei and/or the proportion of specimen area that has been stained needs to be measured. Pure visual estimates of this provide very crude results with poor inter- and intraobserver reproducibility. For this purpose computerised image analysis based methods are needed. The methods presented in this thesis aim to make the quantification objective and reproducible.

    A new supervised method for computing a pixelwise box classifier has been developed. The resulting classifier can be applied to images of the same type as the training image as long as the lighting conditions have not been changed. The main advantage of this method is that time will be saved if there are many similar images to classify, since box/classification is a fast method.

    In order to reduce user interaction, automatic methods for classification, based on more specific knowledge about the images, were developed. These methods include automatic classification of two types of roundish objects, e.g. cell nuclei, on a lighter background, first without, and then with the help of reference images of external cultured cells stained together with the specimen. A method for automatic segmentation of dark thin structures, e.g. microvessels, has been developed as well.

    A characteristic of all these methods is that they are implemented as a sequence of single colour band operations, instead of multiband operations. The purpose of this is to make the operations simple and efficient.

  • 24.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis1997Other (Other academic)
  • 25.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Egevad, Lars
    Nordin, Bo
    Bengtsson, Ewert
    A new method for segmentation of colour images applied to immunohistochemically stained cell nuclei1997In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 15, no 3, p. 145-156Article in journal (Refereed)
    Abstract [en]

    A new method for segmenting images of immunohistochemically stained cell nuclei is presented. The aim is to distinguish between cell nuclei with a positive staining reaction and other cell nuclei, and to make it possible to quantify the reaction. First, a

  • 26.
    Ranefall, Petter
    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.
    Kecheril Sadanandan, Sajith
    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.
    Ishaq, Omer
    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.
    Matuszewski, Damian
    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.
    Sintorn, Ida-Maria
    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.
    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.
    Large-Scale Analysis of Cells and Tissue2014Conference paper (Other academic)
  • 27.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Nordin, Bo
    Bengtsson, Ewert
    A New Method for Creating a Pixelwise Box classifier for Colour Images1997In: Machine Graphics and Vision, ISSN 1230-0535, Vol. 6, no 3, p. 305-323Article in journal (Refereed)
  • 28.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Nordin, Bo
    Bengtsson, Ewert
    Finding Facial Features Using an HLS Colour Space1995In: 8th International Conference on Image Analysis and Processing, Springer Verlag , 1995, p. 191-196Conference paper (Refereed)
  • 29.
    Ranefall, Petter
    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.
    Pacureanu, Alexandra
    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.
    Avenel, Christophe
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction.
    Carpenter, Anne E.
    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.
    The Giga-pixel Challenge: Full Resolution Image Analysis – Without Losing the Big Picture: An open-source approach for multi-scale analysis and visualization of slide-scanner data2014Conference paper (Other academic)
    Abstract [en]

    The amount of image data in a slide scanner image is usually in the giga-pixel range, inducing challenges for image analysis and visualization. At the same time, in order to maximize the information gained in these experi-ments and integrate expert knowledge with quantitative measurements, it is crucial to maintain the connection be-tween high-resolution per-cell and per-region metrics with lower resolution relational metrics. We present a free and open-source framework for full resolution image analysis of large images, e.g. slide scanner data, with the possibility of visual examination and interaction at multiple resolutions. The interface enables seamless zooming and panning, with the option to toggle multiple layers, such as segmentation masks and classification results, on or off. We make use of the strength and flexibility of existing state-of-the-art open-source software for visualization, creating resolution pyra-mids, image registration and image analysis.

    Download full text (pdf)
    fulltext
  • 30.
    Ranefall, Petter
    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, 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.
    Pacureanu, Alexandra
    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. 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, 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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
    Viewing and analyzing slide scanner data using CellProfiler (work in progress)2013In: European BioImage Analysis Symposium 2013 / [ed] Julien Colombelli, 2013, p. 58-Conference paper (Other academic)
    Abstract [en]

    Viewing and analyzing slide scanner data using CellProfiler (work in progress)

    Petter Ranefall1, Alexandra Pacureanu1, and Carolina Wählby1,2

    1Centre for Image Analysis, Department of Information Technology, Uppsala University, and Science for Life Laboratory, Sweden

    2Imaging Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA

     

    The amount of image data in a slide scanner image is usually very large, inducing challenges for image analysis and visualization. We would like to use the strengths and flexibility of CellProfiler for the image analysis, but performing high-resolution image analysis on large slide scanner images is unfortunately not possible due to memory limitations. At the same time, we would like to visualize the results of the analysis in the context of the full tissue slide so that for example phenotypic variations at the sub-cellular level can be related to lower-resolution structures such as vessels, ducts, or tumors in the tissue.

     

    To approach this problem we split the image into smaller tiles that are more suitable for CellProfiler to handle. By keeping track of the coordinates of the tiles we can display the results on the original, full-size image. Our aim is to enable visual examination at multiple resolutions and with the option to toggle results such as segmentation masks and classification results on or off using a “Google Maps” type of view.

     

    In particular, we work with tissue profiling by in situ sequencing of RNA molecules. The tissue samples have to be removed from the microscope for each new sequencing cycle. In this case, and in other applications dealing with repeated staining, there are often differences in the alignment between the imaging rounds. We assume that these misalignments are rigid (only translation and rotation), and we have added an image registration step in order to align the different channels before partitioning the images into tiles suitable for analysis in CellProfiler. 

  • 31.
    Ranefall, Petter
    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.
    Sadanandan, Sajith Kecheril
    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, Computerized Image Analysis and Human-Computer Interaction.
    Fast Adaptive Local Thresholding Based on Ellipse fit2016Conference paper (Refereed)
    Abstract [en]

    In this paper we propose an adaptive thresholding method where each object is thresholded optimizing its shape. The method is based on a component tree representation, which can be computed in quasi-linear time. We test and evaluate the method on images of bacteria from three different live-cell analysis experiments and show that the proposed method produces segmentation results comparable to state-of-the-art but at least an order of magnitude faster. The method can be extended to compute any feature measurements that can be calculated in a cumulative way, and holds great potential for applications where a priori information on expected object size and shape is available.

  • 32.
    Ranefall, Petter
    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.
    Sadanandan, Sajith Kecheril
    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, 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.
    Global And Local Adaptive Gray-level Thresholding Based on Object Features2016In: Swedish Symposium on Image Analysis 2016 / [ed] Robin Strand, 2016Conference paper (Other academic)
    Abstract [en]

    In this paper we propose a) a fast and robustglobal gray-level thresholding method based on object size,where the selection of threshold level is based on recall andmaximum precision with regard to objects within a givensize interval, and b) an adaptive thresholding method whereeach object is thresholded optimizing its shape. The methodsare based on on the component tree representation, whichcan be computed in quasi-linear time. We show that forreal images of cell nuclei and synthetic data sets mimickingfluorescent spots the proposed methods are more robust thanall standard global thresholding methods in ImageJ andCellProfiler. The methods can be extended to compute anyfeature measurements that can be calculated in a cumulativeway, and hold great potential for applications where a prioriinformation on expected object size and shape is available.

  • 33.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, Kenneth
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, Ann-Catrin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Automatic quantification of immunohistochemically stained cell nuclei based on standard reference cells1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, p. 111-23Article in journal (Refereed)
    Abstract [en]

    A fully automatic method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions, is presented. Agarose embedded cultured fibroblasts were fixed, paraffin embedded and sectioned at 4 microm. They were then stained together with 4 microm sections of the test specimen obtained from bladder cancer material. A colour based classifier is automatically computed from the control cells. The method was tested on formalin fixed paraffin embedded tissue section material, stained with monoclonal antibodies against the Ki67 antigen and cyclin A protein. Ki67 staining results in a detailed nuclear texture with pronounced nucleoli and cyclin A staining is obtained in a more homogeneously distributed pattern. However, different staining patterns did not seem to influence labelling index quantification, and the sensitivity to variations in light conditions and choice of areas within the control population was low. Thus, the technique represents a robust and reproducible quantification method. In tests measuring proportions of stained area an average standard deviation of about 1.5% for the same field was achieved when classified with classifiers created from different control samples.

  • 34.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, Kenneth
    Bengtsson, Ewert
    Automatic quantification of immunohistochemically stained cell nuclei using unsupervised image analysis1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 16, no 1, p. 29-43Article in journal (Refereed)
    Abstract [en]

    A method for quantification of images of immunohistochemically stained cell nuclei by computing area proportions is presented. The image is transformed by a principal component transform. The resulting first component image is used to segment the objects

  • 35. Ranefall, Petter
    et al.
    Wester, Kenneth
    Busch, Christer
    Malmstrom, Per-Uno
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
    Bengtsson, E
    Automatic quantification of microvessels using unsupervised image analysis.1998In: Anal. Cell. Pathol., Vol. 16, p. 29-Article in journal (Refereed)
  • 36.
    Ranefall, Petter
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wester, Kenneth
    Busch, Christer
    Malmström, Per-Uno
    Bengtsson, Ewert
    Automatic quantification of microvessels using unsupervised image analysis1998In: Analytical Cellular Pathology, ISSN 0921-8912, E-ISSN 1878-3651, Vol. 17, no 2, p. 83-92Article in journal (Refereed)
    Abstract [en]

    An automatic method for quantification of images of microvessels by computing area proportions and number of objects is presented. The objects are segmented from the background using dynamic thresholding of the average component size histogram. To be able

  • 37.
    Ranefall, Petter
    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.
    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.
    Global Gray-level Thresholding Based on Object Size2016In: Cytometry Part A, ISSN 1552-4922, E-ISSN 1552-4930, Vol. 89A, no 4, p. 385-390Article in journal (Refereed)
    Abstract [en]

    In this article, we propose a fast and robust global gray-level thresholding method based on object size, where the selection of threshold level is based on recall and maximum precision with regard to objects within a given size interval. The method relies on the component tree representation, which can be computed in quasi-linear time. Feature-based segmentation is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. We show that for real images of cell nuclei and synthetic data sets mimicking fluorescent spots the proposed method is more robust than all standard global thresholding methods available for microscopy applications in ImageJ and CellProfiler. The proposed method, provided as ImageJ and CellProfiler plugins, is simple to use and the only required input is an interval of the expected object sizes.

  • 38.
    Ranefall, Petter
    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.
    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.
    Your New Default  Thresholding Method?: A robust global gray-level thresholding method based on object features2015In: BioImage Informatics Conference 2015 / [ed] Peter Bajcsy, 2015Conference paper (Other academic)
    Abstract [en]

    We present a new robust method for global gray-level thresholding. The method is based on object features and the input is an interval of the expected object sizes. It is especially suitable for biomedical microscopy applications where objects often vary in number, but have limited variation in size. Our vision is that this method should be the first thresholding method you try when designing a pipeline for object segmentation, and thus your new default method.

  • 39.
    Ranefall, Petter
    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.
    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.
    Bengtsson, Ewert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections2016Conference paper (Other academic)
    Abstract [en]

    Aim

    This work describes a proof-of-principle study within the Exchange of Diagnostic Images in Networks (ExDIN) project, for automatic grading of breast cancer from whole slide images of Ki67 stained tissue sections. The idea was to mimic the manual grading process: “The assessment is carried out on invasive cancer within the area with the highest number of Ki67-positive cancer cell nuclei/area (hot spot), containing at least 200 cells.”

    Method

    • Color deconvolution to separate the image into brown and blue channels.

    • Extract the 10 subsampled tiles (size corresponding to ~200 cells) with the highest values for pre-defined texture and color features.

    • Analyze these tiles in full resolution and compute the maximum positivity (defined as area of positive cells in relation to total cell area, rather than number of cells, since that will speed up the computations and avoid introducing errors due to over- or under segmentation of connected objects).

       

       

       

       

       

       

       

       

       

       

       

       

       

    Figure 1. Illustration of the procedure. Hot spot candidates are extracted from low resolution tiles. Then the final hot spot is selected among the corresponding full resolution versions.

    The results show good correlation to manual estimates and the procedure takes ~4 minutes/slide.

    Future improvements

    • Rules and features defined using machine learning based on training samples given by pathologists.

    • User interface where suggested regions can be deselected manually.

  • 40.
    Smirnova, Anna
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Environmental toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Reproductive Biology in Uppsala (CRU).
    Mentor, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Environmental toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Reproductive Biology in Uppsala (CRU).
    Ranefall, Petter
    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. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.
    Bornehag, Carl-Gustaf
    Public Health Sciences, Karlstad University; Icahn School of Medicine at Mount Sinai.
    Brunström, Björn
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Environmental toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Reproductive Biology in Uppsala (CRU).
    Mattsson, Anna
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Environmental toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Reproductive Biology in Uppsala (CRU).
    Jönsson, Maria
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Organismal Biology, Environmental toxicology. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Medicinska och farmaceutiska vetenskapsområdet, centrumbildningar mm, Centre for Reproductive Biology in Uppsala (CRU).
    Increased apoptosis, reduced Wnt/β-catenin signaling, and altered tail development in zebrafish embryos exposed to a human-relevant chemical mixture2021In: Chemosphere, ISSN 0045-6535, E-ISSN 1879-1298, Vol. 264, no 1, article id 128467Article in journal (Refereed)
    Abstract [en]

    A wide variety of anthropogenic chemicals is detected in humans and wildlife and the health effects of various chemical exposures are not well understood. Early life stages are generally the most susceptible to chemical disruption and developmental exposure can cause disease in adulthood, but the mechanistic understanding of such effects is poor. Within the EU project EDC-MixRisk, a chemical mixture (Mixture G) was identified in the Swedish pregnancy cohort SELMA by the inverse association between levels in women at around gestational week ten with birth weight of their children. This mixture was composed of mono-ethyl phthalate, mono-butyl phthalate, mono-benzyl phthalate, mono-ethylhexyl phthalate, mono-isononyl phthalate, triclosan, perfluorohexane sulfonate, perfluorooctanoic acid, and perfluorooctane sulfonate. In a series of experimental studies, we characterized effects of Mixture G on early development in zebrafish models. Here, we studied apoptosis and Wnt/β-catenin signaling which are two evolutionarily conserved signaling pathways of crucial importance during development. We determined effects on apoptosis by measuring TUNEL staining, caspase-3 activity, and acridine orange staining in wildtype zebrafish embryos, while Wnt/β-catenin signaling was assayed using a transgenic line expressing an EGFP reporter at β-catenin-regulated promoters. We found that Mixture G increased apoptosis, suppressed Wnt/β-catenin signaling in the caudal fin, and altered the shape of the caudal fin at water concentrations only 20–100 times higher than the geometric mean serum concentration in the human cohort. These findings call for awareness that pollutant mixtures like mixture G may interfere with a variety of developmental processes, possibly resulting in adverse health effects.

    Download full text (pdf)
    fulltext
  • 41.
    Thurfjell, Lennart
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Ranefall, Petter
    Bengtsson, Ewert
    A Deformable Atlas of the Chest Based on the Visible Man1998In: Machine Graphics and Vision, ISSN 1230-0535, Vol. 7, no 1-2, p. 176-186Article in journal (Refereed)
  • 42.
    Wang, Yizhi
    et al.
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    Wang, Congchao
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    Ranefall, Petter
    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.
    Broussard, Gerard Joey
    Princeton Univ, Dept Mol Biol, Princeton, NJ 08544 USA.;Princeton Univ, Princeton Neurosci Inst, Princeton, NJ 08544 USA..
    Wang, Yinxue
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    Shi, Guilai
    Univ Calif Davis, Sch Med, Dept Biochem & Mol Med, Sacramento, CA 95817 USA..
    Lyu, Boyu
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    Wu, Chiung-Ting
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    Wang, Yue
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    Tian, Lin
    Univ Calif Davis, Sch Med, Dept Biochem & Mol Med, Sacramento, CA 95817 USA..
    Yu, Guoqiang
    Virginia Polytech Inst & State Univ, Bradley Dept Elect & Comp Engn, Blacksburg, VA 22203 USA..
    SynQuant: an automatic tool to quantify synapses from microscopy images2020In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 36, no 5, p. 1599-1606Article in journal (Refereed)
    Abstract [en]

    Motivation: Synapses are essential to neural signal transmission. Therefore, quantification of synapses and related neurites from images is vital to gain insights into the underlying pathways of brain functionality and diseases. Despite the wide availability of synaptic punctum imaging data, several issues are impeding satisfactory quantification of these structures by current tools. First, the antibodies used for labeling synapses are not perfectly specific to synapses. These antibodies may exist in neurites or other cell compartments. Second, the brightness of different neurites and synaptic puncta is heterogeneous due to the variation of antibody concentration and synapse-intrinsic differences. Third, images often have low signal to noise ratio due to constraints of experiment facilities and availability of sensitive antibodies. These issues make the detection of synapses challenging and necessitates developing a new tool to easily and accurately quantify synapses.

    Results: We present an automatic probability-principled synapse detection algorithm and integrate it into our synapse quantification tool SynQuant. Derived from the theory of order statistics, our method controls the false discovery rate and improves the power of detecting synapses. SynQuant is unsupervised, works for both 2D and 3D data, and can handle multiple staining channels. Through extensive experiments on one synthetic and three real datasets with ground truth annotation or manually labeling, SynQuant was demonstrated to outperform peer specialized unsupervised synapse detection tools as well as generic spot detection methods.

  • 43.
    Wester, K
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, AC
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Ranefall, P
    Bengtsson, E
    Malmstrom, PU
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
    Busch, C
    Cultured human fibroblasts in agarose gel as a multi-functional controlfor immunohistochemistry. Standardization Of Ki67 (MIB1) assessment inroutinely processed urinary bladder carcinoma tissue.2000In: J Pathol, Vol. 190, p. 503-Article in journal (Refereed)
  • 44.
    Wester, K
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Ranefall, P
    Bengtsson, E
    Busch, C
    Malmstrom, PU
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
    Automatic quantification of microvessel density in urinary bladdercarcinoma.1999In: Br J Cancer, Vol. 81, p. 1363-Article in journal (Refereed)
  • 45.
    Wester, K
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Wahlund, E
    Sundstrom, C
    Ranefall, P
    Bengtsson, E
    Russell, PJ
    Ow, KT
    Malmstrom, PU
    Busch, C
    Paraffin section storage and immunohistochemistry - Effects of time, temperature, fixation, and retrieval protocol with emphasis on p53 protein and MIB1 antigen2000In: APPLIED IMMUNOHISTOCHEMISTRY & MOLECULAR MORPHOLOGY, ISSN 1062-3345, Vol. 8, no 1, p. 61-70Article in journal (Refereed)
    Abstract [en]

    It has been observed that immunoreactivity in paraffin sections decreased during storage. In this study, stored paraffin sections from both biopsy material and cultured cells were assessed for changes in immunoreactivity, using color-based image analysis

  • 46.
    Wester, K
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Wahlund, E
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Sundstrom, C
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Ranefall, P
    Bengtsson, E
    Russell, PJ
    Ow, KT
    Malmstrom, PU
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
    Busch, C
    Paraffin section storage and immunohistochemistry. Effects of time,temperature, fixation, and retrieval protocol with emphasis on p53 proteinand MIB1 antigen.2000In: Appl. Immunohistochem., Vol. 8, p. 61-Article in journal (Refereed)
  • 47.
    Wester, Kenneth
    et al.
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Andersson, Ann-Catrin
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Evert
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Malmström, Per-Uno
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences.
    Busch, Christer
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Genetics and Pathology.
    Cultured human fibroblasts in agarose gel as a multi-functional control for immunohistochemistry: Standardization Of Ki67 (MIB1) assessment in routinely processed urinary bladder carcinoma tissue2000In: Journal of Pathology, ISSN 0022-3417, E-ISSN 1096-9896, Vol. 190, no 4, p. 503-11Article in journal (Refereed)
    Abstract [en]

    Immunohistochemistry (IHC) in clinical practice is hampered by lack of standardization and by subjectivity in interpretation and quantitation. This study aimed to develop a control system for IHC in routinely fixed and histoprocessed tissues. Such a system should be easy to handle in clinical practice and should reflect variations in fixation time, section thickness, section storage conditions, and staining protocols. In addition, in image analysis quantitation of immunostained tissues, when using classifiers computed on IHC-control images, the control system should be very stable. Cultured human fibroblasts were suspended in agarose, transferred into a length of tubing and stored at 4 degrees C. Three pieces of the cellgel control were separately fixed, histoprocessed, and paraffin-embedded as external controls. One piece was prepared together with each of 18 bladder carcinoma biopsies as internal controls. Slides with sections from the biopsy and all types of cellgel controls were stored at different temperatures and then stained using three different IHC protocols. The fibroblasts were homogeneously distributed in the agarose gel. Variation in section thickness did not influence immunostaining as evaluated by the MIB1 labelling index (MIB1 LI). The external controls decreased notably in MIB1 LI with increased fixation time. This was not seen in the 18 internal controls that were each fixed with a fresh biopsy. However, section storage and immunostaining conditions influenced the MIB1 expression equally in all control types and to a similar degree to the biopsies. Furthermore, colour-based image analysis quantitation of MIB1 LI in biopsies proved stable and independent of the control type used to compute the classifier.

  • 48.
    Wester, Kenneth
    et al.
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Centre for Image Analysis.
    Ranefall, Petter
    Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis.
    Bengtsson, Ewert
    Busch, Christer
    Malmström, Per-Uno
    Automatic quantification of microvessel density in urinary bladder carcinoma1999In: British Journal of Cancer, ISSN 0007-0920, E-ISSN 1532-1827, Vol. 81, no 8, p. 1363-1370Article in journal (Refereed)
    Abstract [en]

    Seventy-three TUR-T biopsies from bladder carcinoma were evaluated regarding microvessel density, defined as microvessel number (nMVD) and cross-section endothelial cell area (aMVD). A semi-automatic and a newly developed, automatic image analysis techniq

  • 49.
    Wu, Chi-Chih
    et al.
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
    Klaesson, Axel
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Buskas, Julia
    Uppsala University, Science for Life Laboratory, SciLifeLab. Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology.
    Ranefall, Petter
    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.
    Mirzazadeh, Reza
    Söderberg, Ola
    Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
    Wolf, Jochen B. W.
    Uppsala University, Disciplinary Domain of Science and Technology, Biology, Department of Ecology and Genetics, Evolutionary Biology. Uppsala University, Science for Life Laboratory, SciLifeLab.
    In situ quantification of individual mRNA transcripts in melanocytes discloses gene regulation of relevance to speciation2019In: Journal of Experimental Biology, ISSN 0022-0949, E-ISSN 1477-9145, Vol. 222, no 5Article in journal (Refereed)
1 - 49 of 49
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf