{"entity": "researcher", "timestamp": "2026-07-15T07:54:41.767Z", "family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "affiliations": ["Centre for Image AnalysisUppsala University Uppsala 75124 Sweden", "BioImage Informatics Facility of SciLifeLab Uppsala 75124 Sweden"], "links": {"self": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}, "display": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29"}}, "publications": [{"entity": "publication", "iuid": "a596dad743e74d2c803822ac42670a7d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/a596dad743e74d2c803822ac42670a7d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/a596dad743e74d2c803822ac42670a7d"}}, "title": "Spatial mapping of proteins and their activity states in cancer models by multiplex in situ PLA", "authors": [{"family": "L\u00f6f", "given": "Liza", "initials": "L"}, {"family": "Xu", "given": "Bo", "initials": "B", "orcid": "0000-0002-5341-1722", "researcher": {"href": "https://publications.scilifelab.se/researcher/d262ebec52004d1f8bc77d43cafe0cc7.json"}}, {"family": "Sinha", "given": "Tanay Kumar", "initials": "TK"}, {"family": "Dahlstr\u00f6m", "given": "Caroline", "initials": "C"}, {"family": "Vennberg", "given": "Jonas", "initials": "J", "orcid": "0000-0002-8153-9934", "researcher": {"href": "https://publications.scilifelab.se/researcher/3ce37c5833d9402282f308ca16cfe3bb.json"}}, {"family": "Larsson Forss\u00e9n", "given": "Tore", "initials": "T"}, {"family": "Klaesson", "given": "Axel", "initials": "A"}, {"family": "Clausson", "given": "Carl Magnus", "initials": "CM"}, {"family": "Wang", "given": "Xuan", "initials": "X", "orcid": "0000-0002-6672-017X", "researcher": {"href": "https://publications.scilifelab.se/researcher/ea013f2f56894e698a1bce01a49dcf7a.json"}}, {"family": "Str\u00f6mberg-Olsson", "given": "Ulla", "initials": "U"}, {"family": "Kamali-Moghaddam", "given": "Masood", "initials": "M", "orcid": "0000-0002-1303-2218", "researcher": {"href": "https://publications.scilifelab.se/researcher/290dd535fb414c68bc49a8a2b7995770.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Zieba-Wicher", "given": "Agata", "initials": "A"}, {"family": "Landegren", "given": "Ulf", "initials": "U", "orcid": "0000-0002-7820-1000", "researcher": {"href": "https://publications.scilifelab.se/researcher/87392e51288f4ef9a38fe3989d10d180.json"}}], "type": "posted-content", "published": "2025-07-11", "journal": {"title": "biorxiv", "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": null}, "abstract": null, "doi": "10.1101/2025.07.11.662357", "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative", "Spatial Proteomics": "Service"}, "xrefs": [], "notes": [], "created": "2025-11-25T12:57:35.337Z", "modified": "2026-03-06T09:57:26.506Z"}, {"entity": "publication", "iuid": "085e2de78b514987b28baa61cb116ff1", "links": {"self": {"href": "https://publications.scilifelab.se/publication/085e2de78b514987b28baa61cb116ff1.json"}, "display": {"href": "https://publications.scilifelab.se/publication/085e2de78b514987b28baa61cb116ff1"}}, "title": "Combining spatial transcriptomics with tissue morphology.", "authors": [{"family": "Chelebian", "given": "Eduard", "initials": "E", "orcid": "0000-0001-6852-6605", "researcher": {"href": "https://publications.scilifelab.se/researcher/278694af6d7e499f9432c6523da24f25.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2025-05-13", "journal": {"title": "Nat Commun", "issn": "2041-1723", "volume": "16", "issue": "1", "pages": "4452", "issn-l": "2041-1723"}, "abstract": "Spatial transcriptomics has transformed our understanding of tissue architecture by preserving the spatial context of gene expression patterns. Simultaneously, advances in imaging AI have enabled extraction of morphological features describing the tissue. This review introduces a framework for categorizing methods that combine spatial transcriptomics with tissue morphology, focusing on either translating or integrating morphological features into spatial transcriptomics. Translation involves using morphology to predict gene expression, creating super-resolution maps or inferring genetic information from H&E-stained samples. Integration enriches spatial transcriptomics by identifying morphological features that complement gene expression. We also explore learning strategies and future directions for this emerging field.", "doi": "10.1038/s41467-025-58989-8", "pmid": "40360467", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC12075478"}, {"db": "pii", "key": "10.1038/s41467-025-58989-8"}], "notes": [], "created": "2025-11-25T12:53:04.988Z", "modified": "2025-11-25T12:53:05.109Z"}, {"entity": "publication", "iuid": "87266d8a6f284e2198986f59a2abeff9", "links": {"self": {"href": "https://publications.scilifelab.se/publication/87266d8a6f284e2198986f59a2abeff9.json"}, "display": {"href": "https://publications.scilifelab.se/publication/87266d8a6f284e2198986f59a2abeff9"}}, "title": "Optimizing Xenium In Situ data utility by quality assessment and best-practice analysis workflows.", "authors": [{"family": "Marco Salas", "given": "Sergio", "initials": "S", "orcid": "0000-0002-4636-0322", "researcher": {"href": "https://publications.scilifelab.se/researcher/2db8123d7b0f47afbb06b0559fcd79ff.json"}}, {"family": "Kuemmerle", "given": "Louis B", "initials": "LB", "orcid": "0000-0002-9193-1243", "researcher": {"href": "https://publications.scilifelab.se/researcher/4d339771ed9a4559ad56a63e9e4e9e80.json"}}, {"family": "Mattsson-Langseth", "given": "Christoffer", "initials": "C"}, {"family": "Tismeyer", "given": "Sebastian", "initials": "S"}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Hu", "given": "Taobo", "initials": "T", "orcid": "0000-0001-5124-7167", "researcher": {"href": "https://publications.scilifelab.se/researcher/b693c14571864cfcb6f3b36cce183f12.json"}}, {"family": "Rehman", "given": "Habib", "initials": "H", "orcid": "0000-0002-8671-982X", "researcher": {"href": "https://publications.scilifelab.se/researcher/dff0d9994c0f4237a5afbcd66d706dbf.json"}}, {"family": "Grillo", "given": "Marco", "initials": "M", "orcid": "0000-0003-2155-0645", "researcher": {"href": "https://publications.scilifelab.se/researcher/bcd8bec6567444558acbcbd1b3d28f7a.json"}}, {"family": "Czarnewski", "given": "Paulo", "initials": "P"}, {"family": "Helgadottir", "given": "Saga", "initials": "S"}, {"family": "Tiklova", "given": "Katarina", "initials": "K"}, {"family": "Andersson", "given": "Axel", "initials": "A", "orcid": "0000-0002-4714-3127", "researcher": {"href": "https://publications.scilifelab.se/researcher/34027f6b47684ff4be205e2d547c5f8a.json"}}, {"family": "Rafati", "given": "Nima", "initials": "N"}, {"family": "Chatzinikolaou", "given": "Maria", "initials": "M"}, {"family": "Theis", "given": "Fabian J", "initials": "FJ", "orcid": "0000-0002-2419-1943", "researcher": {"href": "https://publications.scilifelab.se/researcher/15139e290953411590201d9bb402da1f.json"}}, {"family": "Luecken", "given": "Malte D", "initials": "MD", "orcid": "0000-0001-7464-7921", "researcher": {"href": "https://publications.scilifelab.se/researcher/40b82068719d43448992741da54e00bd.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ishaque", "given": "Naveed", "initials": "N", "orcid": "0000-0002-8426-901X", "researcher": {"href": "https://publications.scilifelab.se/researcher/38be333e07614a13bf8c559e7935381a.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}], "type": "journal article", "published": "2025-04-00", "journal": {"title": "Nat. Methods", "issn": "1548-7105", "volume": "22", "issue": "4", "pages": "813-823", "issn-l": "1548-7091"}, "abstract": "The Xenium In Situ platform is a new spatial transcriptomics product commercialized by 10x Genomics, capable of mapping hundreds of genes in situ at subcellular resolution. Given the multitude of commercially available spatial transcriptomics technologies, recommendations in choice of platform and analysis guidelines are increasingly important. Herein, we explore 25 Xenium datasets generated from multiple tissues and species, comparing scalability, resolution, data quality, capacities and limitations with eight other spatially resolved transcriptomics technologies and commercial platforms. In addition, we benchmark the performance of multiple open-source computational tools, when applied to Xenium datasets, in tasks including preprocessing, cell segmentation, selection of spatially variable features and domain identification. This study serves as an independent analysis of the performance of Xenium, and provides best practices and recommendations for analysis of such datasets.", "doi": "10.1038/s41592-025-02617-2", "pmid": "40082609", "labels": {"Bioinformatics (NBIS)": "Collaborative", "Bioinformatics Support and Infrastructure": "Collaborative", "Bioinformatics Support, Infrastructure and Training": "Collaborative", "BioImage Informatics": "Collaborative", "In Situ Sequencing": "Technology development"}, "xrefs": [{"db": "pmc", "key": "PMC11978515"}, {"db": "pii", "key": "10.1038/s41592-025-02617-2"}], "notes": [], "created": "2025-11-21T12:56:02.504Z", "modified": "2025-11-28T06:50:06.581Z"}, {"entity": "publication", "iuid": "b9a52931d3d74346a7d1d9534c62ab91", "links": {"self": {"href": "https://publications.scilifelab.se/publication/b9a52931d3d74346a7d1d9534c62ab91.json"}, "display": {"href": "https://publications.scilifelab.se/publication/b9a52931d3d74346a7d1d9534c62ab91"}}, "title": "A clinical prostate biopsy dataset with undetected cancer.", "authors": [{"family": "Chelebian", "given": "Eduard", "initials": "E", "orcid": "0000-0001-6852-6605", "researcher": {"href": "https://publications.scilifelab.se/researcher/278694af6d7e499f9432c6523da24f25.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "J\u00e4remo", "given": "Helena", "initials": "H"}, {"family": "Andersson", "given": "Pernilla", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Bergh", "given": "Anders", "initials": "A"}], "type": "journal article", "published": "2025-03-11", "journal": {"title": "Sci Data", "issn": "2052-4463", "volume": "12", "issue": "1", "pages": "423", "issn-l": "2052-4463"}, "abstract": "Prostate cancer is a heterogeneous disease showing variability both among individuals and within a patient. While most cases are indolent, aggressive tumors require early intervention. Accurately predicting tumor behavior is challenging, contributing to overdiagnosis but also undertreatment. Current imaging methods may miss the most malignant areas, leading to biopsies often capturing non-malignant prostate tissue even if cancer is present elsewhere in the organ. This non-malignant tissue, however, holds potential as a source for novel diagnostic and prognostic markers. Our clinical dataset comprises men with raised prostate-specific antigen but whose initial prostate needle biopsies only contained benign tissue. Half of the paired patients remained cancer-free for over eight years, while the others were diagnosed with prostate cancer within 30 months of follow-up. We share these initial benign biopsies to enable the exploration of morphological changes in non-malignant tissue and the potential for improved diagnostic accuracy in the early identification of patients with prostate cancer.", "doi": "10.1038/s41597-025-04758-7", "pmid": "40069192", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC11897396"}, {"db": "pii", "key": "10.1038/s41597-025-04758-7"}], "notes": [], "created": "2025-11-25T12:53:53.705Z", "modified": "2025-11-25T12:53:53.733Z"}, {"entity": "publication", "iuid": "fdc492dc154745b7be16941105050b7a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fdc492dc154745b7be16941105050b7a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fdc492dc154745b7be16941105050b7a"}}, "title": "Spatial Transcriptome Mapping of the Desmoplastic Growth Pattern of Colorectal Liver Metastases by In Situ Sequencing Reveals a Biologically Relevant Zonation of the Desmoplastic Rim.", "authors": [{"family": "Andersson", "given": "Axel", "initials": "A", "orcid": "0000-0002-4714-3127", "researcher": {"href": "https://publications.scilifelab.se/researcher/34027f6b47684ff4be205e2d547c5f8a.json"}}, {"family": "Escriva Conde", "given": "Maria", "initials": "M", "orcid": "0009-0005-3891-8794", "researcher": {"href": "https://publications.scilifelab.se/researcher/875766b30dd64a1688a73573ce0e878d.json"}}, {"family": "Surova", "given": "Olga", "initials": "O", "orcid": "0009-0004-8554-9779", "researcher": {"href": "https://publications.scilifelab.se/researcher/447fc7a99f9b41c5878ce791ce956b81.json"}}, {"family": "Vermeulen", "given": "Peter", "initials": "P", "orcid": "0000-0002-5676-3141", "researcher": {"href": "https://publications.scilifelab.se/researcher/11f4ffda52ef4e2bab3cb7522a81af57.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "Nystr\u00f6m", "given": "Hanna", "initials": "H", "orcid": "0000-0003-4595-5399", "researcher": {"href": "https://publications.scilifelab.se/researcher/d7f084a7659745c89ee11deabc737627.json"}}], "type": "journal article", "published": "2024-10-01", "journal": {"title": "Clin. Cancer Res.", "issn": "1557-3265", "volume": "30", "issue": "19", "pages": "4517-4529", "issn-l": "1078-0432"}, "abstract": "We describe the fibrotic rim formed in the desmoplastic histopathologic growth pattern (DHGP) of colorectal cancer liver metastasis (CLM) using in situ sequencing (ISS). The origin of the desmoplastic rim is still a matter of debate, and the detailed cellular organization has not yet been fully elucidated. Understanding the biology of the DHGP in CLM can lead to targeted treatment and improve survival.\n\nWe used ISS, targeting 150 genes, to characterize the desmoplastic rim by unsupervised clustering of gene coexpression patterns. The cohort comprised 10 chemo-na\u00efve liver metastasis resection samples with a DHGP.\n\nUnsupervised clustering of spatially mapped genes revealed molecular and cellular diversity within the desmoplastic rim. We confirmed the presence of the ductular reaction and cancer-associated fibroblasts. Importantly, we discovered angiogenesis and outer and inner zonation in the rim, characterized by nerve growth factor receptor and periostin expression.\n\nISS enabled the analysis of the cellular organization of the fibrous rim surrounding CLM with a DHGP and suggests a transition from the outer part of the rim, with nonspecific liver injury response, into the inner part, with gene expression indicating collagen synthesis and extracellular matrix remodeling influenced by the interaction with cancer cells, creating a cancer cell-supportive environment. Moreover, we found angiogenic processes in the rim. Our results provide a potential explanation of the origin of the rim in DHGP and lead to exploring novel targeted treatments for patients with CLM to improve survival.", "doi": "10.1158/1078-0432.CCR-23-3461", "pmid": "39052239", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service", "In Situ Sequencing": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC11443209"}, {"db": "pii", "key": "746698"}], "notes": [], "created": "2024-11-13T10:02:29.704Z", "modified": "2025-10-17T13:02:16.499Z"}, {"entity": "publication", "iuid": "b6c5d8a2ae634c4da4ef4891d22c3e88", "links": {"self": {"href": "https://publications.scilifelab.se/publication/b6c5d8a2ae634c4da4ef4891d22c3e88.json"}, "display": {"href": "https://publications.scilifelab.se/publication/b6c5d8a2ae634c4da4ef4891d22c3e88"}}, "title": "Points2Regions: Fast, interactive clustering of imaging-based spatial transcriptomics data.", "authors": [{"family": "Andersson", "given": "Axel", "initials": "A", "orcid": "0000-0002-4714-3127", "researcher": {"href": "https://publications.scilifelab.se/researcher/34027f6b47684ff4be205e2d547c5f8a.json"}}, {"family": "Behanova", "given": "Andrea", "initials": "A"}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Windhager", "given": "Jonas", "initials": "J"}, {"family": "Malmberg", "given": "Filip", "initials": "F"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2024-09-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "volume": "105", "issue": "9", "pages": "677-687", "issn-l": "1552-4922"}, "abstract": "Imaging-based spatial transcriptomics techniques generate data in the form of spatial points belonging to different mRNA classes. A crucial part of analyzing the data involves the identification of regions with similar composition of mRNA classes. These biologically interesting regions can manifest at different spatial scales. For example, the composition of mRNA classes on a cellular scale corresponds to cell types, whereas compositions on a millimeter scale correspond to tissue-level structures. Traditional techniques for identifying such regions often rely on complementary data, such as pre-segmented cells, or lengthy optimization. This limits their applicability to tasks on a particular scale, restricting their capabilities in exploratory analysis. This article introduces \"Points2Regions,\" a computational tool for identifying regions with similar mRNA compositions. The tool's novelty lies in its rapid feature extraction by rasterizing points (representing mRNAs) onto a pyramidal grid and its efficient clustering using a combination of hierarchical and -means clustering. This enables fast and efficient region discovery across multiple scales without relying on additional data, making it a valuable resource for exploratory analysis. Points2Regions has demonstrated performance similar to state-of-the-art methods on two simulated datasets, without relying on segmented cells, while being several times faster. Experiments on real-world datasets show that regions identified by Points2Regions are similar to those identified in other studies, confirming that Points2Regions can be used to extract biologically relevant regions. The tool is shared as a Python package integrated into TissUUmaps and a Napari plugin, offering interactive clustering and visualization, significantly enhancing user experience in data exploration. k", "doi": "10.1002/cyto.a.24884", "pmid": "38958502", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2024-11-13T10:13:09.480Z", "modified": "2024-11-13T10:13:09.510Z"}, {"entity": "publication", "iuid": "c92cbd6e5d684872a9bd87af735238d8", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c92cbd6e5d684872a9bd87af735238d8.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c92cbd6e5d684872a9bd87af735238d8"}}, "title": "Optimizing Xenium In Situ data utility by quality assessment and best practice analysis workflows", "authors": [{"family": "Salas", "given": "Sergio Marco", "initials": "SM", "orcid": "0000-0002-4636-0322", "researcher": {"href": "https://publications.scilifelab.se/researcher/2db8123d7b0f47afbb06b0559fcd79ff.json"}}, {"family": "Czarnewski", "given": "Paulo", "initials": "P", "orcid": "0000-0001-8150-4021", "researcher": {"href": "https://publications.scilifelab.se/researcher/b84309de4e3946159c374ffa6d977560.json"}}, {"family": "Kuemmerle", "given": "Louis B", "initials": "LB", "orcid": "0000-0002-9193-1243", "researcher": {"href": "https://publications.scilifelab.se/researcher/4d339771ed9a4559ad56a63e9e4e9e80.json"}}, {"family": "Helgadottir", "given": "Saga", "initials": "S", "orcid": "0000-0001-8070-147X", "researcher": {"href": "https://publications.scilifelab.se/researcher/98648914489b45dca447d2425a460c99.json"}}, {"family": "Matsson-Langseth", "given": "Christoffer", "initials": "C", "orcid": "0000-0003-2230-8594", "researcher": {"href": "https://publications.scilifelab.se/researcher/df19aaf2ad714a63aa40dc6b18a06229.json"}}, {"family": "Tismeyer", "given": "Sebastian", "initials": "S", "orcid": "0000-0002-0492-1523", "researcher": {"href": "https://publications.scilifelab.se/researcher/73fa7867433147ef927fdd0768e45387.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Rehman", "given": "Habib", "initials": "H", "orcid": "0000-0002-8671-982X", "researcher": {"href": "https://publications.scilifelab.se/researcher/dff0d9994c0f4237a5afbcd66d706dbf.json"}}, {"family": "Tiklova", "given": "Katarina", "initials": "K", "orcid": "0000-0002-9529-4552", "researcher": {"href": "https://publications.scilifelab.se/researcher/14bbad41b8ed42268b71014ce111d247.json"}}, {"family": "Andersson", "given": "Axel", "initials": "A", "orcid": "0000-0002-4714-3127", "researcher": {"href": "https://publications.scilifelab.se/researcher/34027f6b47684ff4be205e2d547c5f8a.json"}}, {"family": "Chatzinikolaou", "given": "Maria", "initials": "M", "orcid": "0000-0002-3539-9088", "researcher": {"href": "https://publications.scilifelab.se/researcher/0fa3519e517549bc854f19983e649074.json"}}, {"family": "Theis", "given": "Fabian J", "initials": "FJ", "orcid": "0000-0002-2419-1943", "researcher": {"href": "https://publications.scilifelab.se/researcher/15139e290953411590201d9bb402da1f.json"}}, {"family": "Luecken", "given": "Malte D", "initials": "MD", "orcid": "0000-0001-7464-7921", "researcher": {"href": "https://publications.scilifelab.se/researcher/40b82068719d43448992741da54e00bd.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ishaque", "given": "Naveed", "initials": "N", "orcid": "0000-0002-8426-901X", "researcher": {"href": "https://publications.scilifelab.se/researcher/38be333e07614a13bf8c559e7935381a.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}], "type": "posted-content", "published": "2023-02-14", "journal": {"title": "biorxiv", "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": null}, "abstract": null, "doi": "10.1101/2023.02.13.528102", "pmid": null, "labels": {"In Situ Sequencing": "Technology development"}, "xrefs": [], "notes": [], "created": "2023-05-31T11:12:14.703Z", "modified": "2025-12-18T19:55:14.931Z"}, {"entity": "publication", "iuid": "77c2cf62014a4e0390043fe01929db7f", "links": {"self": {"href": "https://publications.scilifelab.se/publication/77c2cf62014a4e0390043fe01929db7f.json"}, "display": {"href": "https://publications.scilifelab.se/publication/77c2cf62014a4e0390043fe01929db7f"}}, "title": "Automated detection of vascular remodeling in tumor-draining lymph nodes by the deep-learning tool HEV-finder.", "authors": [{"family": "Bekkhus", "given": "Tove", "initials": "T", "orcid": "0000-0002-2868-2483", "researcher": {"href": "https://publications.scilifelab.se/researcher/b28423917e67447bbb13a50006ed9e4e.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Hanna", "given": "Sabella", "initials": "S"}, {"family": "Franz\u00e9n Boger", "given": "Mathias", "initials": "M"}, {"family": "Klemm", "given": "Anna", "initials": "A", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Bacovia", "given": "Daniel Vasiliu", "initials": "DV"}, {"family": "W\u00e4rnberg", "given": "Fredrik", "initials": "F", "orcid": "0000-0002-0130-7296", "researcher": {"href": "https://publications.scilifelab.se/researcher/25bbcad19da944faa9aa6b32377ae212.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ulvmar", "given": "Maria H", "initials": "MH", "orcid": "0000-0002-9050-0978", "researcher": {"href": "https://publications.scilifelab.se/researcher/c7a0253b26d64189a014719cf1e4ab64.json"}}], "type": "journal article", "published": "2022-09-00", "journal": {"title": "J. Pathol.", "issn": "1096-9896", "issn-l": "0022-3417", "volume": "258", "issue": "1", "pages": "4-11"}, "abstract": "Vascular remodeling is common in human cancer and has potential as future biomarkers for prediction of disease progression and tumor immunity status. It can also affect metastatic sites, including the tumor-draining lymph nodes (TDLNs). Dilation of the high endothelial venules (HEVs) within TDLNs has been observed in several types of cancer. We recently demonstrated that it is a premetastatic effect that can be linked to tumor invasiveness in breast cancer. Manual visual assessment of changes in vascular morphology is a tedious and difficult task, limiting high-throughput analysis. Here we present a fully automated approach for detection and classification of HEV dilation. By using 12,524 manually classified HEVs, we trained a deep-learning model and created a graphical user interface for visualization of the results. The tool, named the HEV-finder, selectively analyses HEV dilation in specific regions of the lymph nodes. We evaluated the HEV-finder's ability to detect and classify HEV dilation in different types of breast cancer compared to manual annotations. Our results constitute a successful example of large-scale, fully automated, and user-independent, image-based quantitative assessment of vascular remodeling in human pathology and lay the ground for future exploration of HEV dilation in TDLNs as a biomarker. \u00a9 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.", "doi": "10.1002/path.5981", "pmid": "35696253", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pmc", "key": "PMC9543492"}], "notes": [], "created": "2022-08-04T08:33:08.492Z", "modified": "2022-11-02T06:20:15.622Z"}, {"entity": "publication", "iuid": "6be7e5cf01494556bde5a433532cc836", "links": {"self": {"href": "https://publications.scilifelab.se/publication/6be7e5cf01494556bde5a433532cc836.json"}, "display": {"href": "https://publications.scilifelab.se/publication/6be7e5cf01494556bde5a433532cc836"}}, "title": "De novo spatiotemporal modelling of cell-type signatures in the developmental human heart using graph convolutional neural networks.", "authors": [{"family": "Marco Salas", "given": "Sergio", "initials": "S"}, {"family": "Yuan", "given": "Xiao", "initials": "X", "orcid": "0000-0001-9788-7717", "researcher": {"href": "https://publications.scilifelab.se/researcher/b115f1a0c1a844e89becb86996dbdf41.json"}}, {"family": "Sylven", "given": "Christer", "initials": "C"}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications.scilifelab.se/researcher/cca1e7c3e70a4d45aacc3a46d14b9cbe.json"}}], "type": "journal article", "published": "2022-08-00", "journal": {"title": "PLoS Comput. Biol.", "issn": "1553-7358", "issn-l": "1553-734X", "volume": "18", "issue": "8", "pages": "e1010366"}, "abstract": "With the emergence of high throughput single cell techniques, the understanding of the molecular and cellular diversity of mammalian organs have rapidly increased. In order to understand the spatial organization of this diversity, single cell data is often integrated with spatial data to create probabilistic cell maps. However, targeted cell typing approaches relying on existing single cell data achieve incomplete and biased maps that could mask the true diversity present in a tissue slide. Here we applied a de novo technique to spatially resolve and characterize cellular diversity of in situ sequencing data during human heart development. We obtained and made accessible well defined spatial cell-type maps of fetal hearts from 4.5 to 9 post conception weeks, not biased by probabilistic cell typing approaches. With our analysis, we could characterize previously unreported molecular diversity within cardiomyocytes and epicardial cells and identified their characteristic expression signatures, comparing them with specific subpopulations found in single cell RNA sequencing datasets. We further characterized the differentiation trajectories of epicardial cells, identifying a clear spatial component on it. All in all, our study provides a novel technique for conducting de novo spatial-temporal analyses in developmental tissue samples and a useful resource for online exploration of cell-type differentiation during heart development at sub-cellular image resolution.", "doi": "10.1371/journal.pcbi.1010366", "pmid": "35960757", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service", "In Situ Sequencing": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC9401155"}, {"db": "pii", "key": "PCOMPBIOL-D-22-00047"}], "notes": [], "created": "2022-08-30T08:59:56.894Z", "modified": "2025-10-17T13:02:17.438Z"}, {"entity": "publication", "iuid": "6f1b382028f74b88ac1be4c6c2cb017d", "links": {"self": {"href": "https://publications.scilifelab.se/publication/6f1b382028f74b88ac1be4c6c2cb017d.json"}, "display": {"href": "https://publications.scilifelab.se/publication/6f1b382028f74b88ac1be4c6c2cb017d"}}, "title": "SimSearch: A Human-in-The-Loop Learning Framework for Fast Detection of Regions of Interest in Microscopy Images.", "authors": [{"family": "Gupta", "given": "Ankit", "initials": "A", "orcid": "0000-0002-9961-1041", "researcher": {"href": "https://publications.scilifelab.se/researcher/ef29c8c1354a4c6d9ca8b0edaa034710.json"}}, {"family": "Sabirsh", "given": "Alan", "initials": "A", "orcid": "0000-0001-5310-0281", "researcher": {"href": "https://publications.scilifelab.se/researcher/0000d5f644194d6f8daf8b7f5dd0540c.json"}}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2022-08-00", "journal": {"title": "IEEE J Biomed Health Inform", "issn": "2168-2208", "issn-l": null, "volume": "26", "issue": "8", "pages": "4079-4089"}, "abstract": "Large-scale microscopy-based experiments often result in images with rich but sparse information content. An experienced microscopist can visually identify regions of interest (ROIs), but this becomes a cumbersome task with large datasets. Here we present SimSearch, a framework for quick and easy user-guided training of a deep neural model aimed at fast detection of ROIs in large-scale microscopy experiments.\n\nThe user manually selects a small number of patches representing different classes of ROIs. This is followed by feature extraction using a pre-trained deep-learning model, and interactive patch selection pruning, resulting in a smaller set of clean (user approved) and larger set of noisy (unapproved) training patches of ROIs and background. The pre-trained deep-learning model is thereafter first trained on the large set of noisy patches, followed by refined training using the clean patches.\n\nThe framework is evaluated on fluorescence microscopy images from a large-scale drug screening experiment, brightfield images of immunohistochemistry-stained patient tissue samples, and malaria-infected human blood smears, as well as transmission electron microscopy images of cell sections. Compared to state-of-the-art and manual/visual assessment, the results show similar performance with maximal flexibility and minimal a priori information and user interaction.\n\nSimSearch quickly adapts to different data sets, which demonstrates the potential to speed up many microscopy-based experiments based on a small amount of user interaction.\n\nSimSearch can help biologists quickly extract informative regions and perform analyses on large datasets helping increase the throughput in a microscopy experiment.", "doi": "10.1109/JBHI.2022.3177602", "pmid": "35609108", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2022-11-02T06:18:25.917Z", "modified": "2023-06-19T12:58:45.375Z"}, {"entity": "publication", "iuid": "601f2cafa2194c0b9554d477b9dacdb2", "links": {"self": {"href": "https://publications.scilifelab.se/publication/601f2cafa2194c0b9554d477b9dacdb2.json"}, "display": {"href": "https://publications.scilifelab.se/publication/601f2cafa2194c0b9554d477b9dacdb2"}}, "title": "Developmental origins of cell heterogeneity in the human lung", "authors": [{"family": "Sountoulidis", "given": "Alexandros", "initials": "A", "orcid": "0000-0002-8837-4642", "researcher": {"href": "https://publications.scilifelab.se/researcher/f49f693f406b4ba28faf373fa67ee683.json"}}, {"family": "Salas", "given": "Sergio Marco", "initials": "SM", "orcid": "0000-0002-4636-0322", "researcher": {"href": "https://publications.scilifelab.se/researcher/2db8123d7b0f47afbb06b0559fcd79ff.json"}}, {"family": "Braun", "given": "Emelie", "initials": "E"}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Bergenstr\u00e5hle", "given": "Joseph", "initials": "J", "orcid": "0000-0002-1136-7719", "researcher": {"href": "https://publications.scilifelab.se/researcher/1cec49beb1cb4ee09871ef7cfe323396.json"}}, {"family": "Vicari", "given": "Marco", "initials": "M", "orcid": "0000-0002-3042-6278", "researcher": {"href": "https://publications.scilifelab.se/researcher/6348d0901b3e44cd80bde25cecd0205b.json"}}, {"family": "Czarnewski", "given": "Paulo", "initials": "P", "orcid": "0000-0001-8150-4021", "researcher": {"href": "https://publications.scilifelab.se/researcher/b84309de4e3946159c374ffa6d977560.json"}}, {"family": "Theelke", "given": "Jonas", "initials": "J", "orcid": "0000-0002-5074-1793", "researcher": {"href": "https://publications.scilifelab.se/researcher/9bfd9c1f49b24c9e8dc5c22bc4288543.json"}}, {"family": "Liontos", "given": "Andreas", "initials": "A", "orcid": "0000-0003-0838-3571", "researcher": {"href": "https://publications.scilifelab.se/researcher/af0596d61a794c188112ba7b256efac6.json"}}, {"family": "Abalo", "given": "Xesus", "initials": "X", "orcid": "0000-0002-1643-0705", "researcher": {"href": "https://publications.scilifelab.se/researcher/944b78e930df40ee8cd5d590638cd4d9.json"}}, {"family": "Andrusivov\u00e1", "given": "\u017daneta", "initials": "\u017d", "orcid": "0000-0002-4350-2524", "researcher": {"href": "https://publications.scilifelab.se/researcher/f89be535ddfa4c6ab46201fb383f4a2b.json"}}, {"family": "Asp", "given": "Michaela", "initials": "M", "orcid": "0000-0001-5941-7220", "researcher": {"href": "https://publications.scilifelab.se/researcher/cf1751a54e274e60b77290464b4d9733.json"}}, {"family": "Li", "given": "Xiaofei", "initials": "X"}, {"family": "Hu", "given": "Lijuan", "initials": "L", "orcid": "0000-0003-1869-0372", "researcher": {"href": "https://publications.scilifelab.se/researcher/49a35358ad6e4b5eaec60d97504102ae.json"}}, {"family": "Sariyar", "given": "Sanem", "initials": "S"}, {"family": "Casals", "given": "Anna Martinez", "initials": "AM", "orcid": "0000-0003-2722-1965", "researcher": {"href": "https://publications.scilifelab.se/researcher/0fa27c4c76374453ba3ffe7eafff4b9a.json"}}, {"family": "Ayoglu", "given": "Burcu", "initials": "B", "orcid": "0000-0001-8277-8999", "researcher": {"href": "https://publications.scilifelab.se/researcher/9ac0b87b8e4548158ac5ea20a08f7a28.json"}}, {"family": "Firsova", "given": "Alexandra", "initials": "A", "orcid": "0000-0002-7345-7429", "researcher": {"href": "https://publications.scilifelab.se/researcher/32fc885aa10d48cebd772ad1470def0c.json"}}, {"family": "Micha\u00eblsson", "given": "Jakob", "initials": "J", "orcid": "0000-0001-6948-7281", "researcher": {"href": "https://publications.scilifelab.se/researcher/75b05fc1689e4d6c9ec0ecd513905a36.json"}}, {"family": "Lundberg", "given": "Emma", "initials": "E", "orcid": "0000-0001-7034-0850", "researcher": {"href": "https://publications.scilifelab.se/researcher/1ffe6259ceb540f385861b5ae52b3055.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sundstr\u00f6m", "given": "Erik", "initials": "E", "orcid": "0000-0003-2931-8015", "researcher": {"href": "https://publications.scilifelab.se/researcher/594c030b77f348e98805ea71e06c1b4d.json"}}, {"family": "Linnarsson", "given": "Sten", "initials": "S", "orcid": "0000-0002-3491-3444", "researcher": {"href": "https://publications.scilifelab.se/researcher/8c0d35942ce042688ea07f23902a8d46.json"}}, {"family": "Lundeberg", "given": "Joakim", "initials": "J", "orcid": "0000-0003-4313-1601", "researcher": {"href": "https://publications.scilifelab.se/researcher/4a4e6ca0f29b4ead8569e2729481c3e0.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "Samakovlis", "given": "Christos", "initials": "C", "orcid": "0000-0002-9153-6040", "researcher": {"href": "https://publications.scilifelab.se/researcher/004a4a166cb34d59ba054055658425f6.json"}}], "type": "posted-content", "published": "2022-01-12", "journal": {"title": "biorxiv", "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": null}, "abstract": null, "doi": "10.1101/2022.01.11.475631", "pmid": null, "labels": {"In Situ Sequencing": "Service"}, "xrefs": [], "notes": [], "created": "2024-11-28T09:49:49.063Z", "modified": "2025-12-18T20:03:09.998Z"}, {"entity": "publication", "iuid": "2071a85470314d6c85780a70fdddb53a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/2071a85470314d6c85780a70fdddb53a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/2071a85470314d6c85780a70fdddb53a"}}, "title": "Morphological Features Extracted by AI Associated with Spatial Transcriptomics in Prostate Cancer.", "authors": [{"family": "Chelebian", "given": "Eduard", "initials": "E", "orcid": "0000-0001-6852-6605", "researcher": {"href": "https://publications.scilifelab.se/researcher/278694af6d7e499f9432c6523da24f25.json"}}, {"family": "Avenel", "given": "Christophe", "initials": "C", "orcid": "0000-0002-1835-921X", "researcher": {"href": "https://publications.scilifelab.se/researcher/5471168acdf94b63b1eab431fd1e8442.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications.scilifelab.se/researcher/da3da754a0264d538a98d8a85874aec1.json"}}, {"family": "Marklund", "given": "Maja", "initials": "M", "orcid": "0000-0003-2627-2437", "researcher": {"href": "https://publications.scilifelab.se/researcher/6a238f7adbc242398a46fd24190a2811.json"}}, {"family": "Tanoglidi", "given": "Anna", "initials": "A", "orcid": "0000-0002-1217-2219", "researcher": {"href": "https://publications.scilifelab.se/researcher/86fcef314379488ba7750f581f4ec5f6.json"}}, {"family": "Mirtti", "given": "Tuomas", "initials": "T", "orcid": "0000-0003-0455-9891", "researcher": {"href": "https://publications.scilifelab.se/researcher/53407e73bd7945af87fcd3b8718fbf4d.json"}}, {"family": "Colling", "given": "Richard", "initials": "R", "orcid": "0000-0001-6344-9081", "researcher": {"href": "https://publications.scilifelab.se/researcher/e817042b24944db1b2fcf74aee34f4e3.json"}}, {"family": "Erickson", "given": "Andrew", "initials": "A", "orcid": "0000-0002-4850-4086", "researcher": {"href": "https://publications.scilifelab.se/researcher/adfe533187e645afa52b17e1f053a82e.json"}}, {"family": "Lamb", "given": "Alastair D", "initials": "AD", "orcid": "0000-0002-2968-7155", "researcher": {"href": "https://publications.scilifelab.se/researcher/798d88b3df1e4a7d994e90b5d60372e6.json"}}, {"family": "Lundeberg", "given": "Joakim", "initials": "J", "orcid": "0000-0003-4313-1601", "researcher": {"href": "https://publications.scilifelab.se/researcher/4a4e6ca0f29b4ead8569e2729481c3e0.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2021-09-28", "journal": {"title": "Cancers (Basel)", "issn": "2072-6694", "volume": "13", "issue": "19", "issn-l": "2072-6694"}, "abstract": "Prostate cancer is a common cancer type in men, yet some of its traits are still under-explored. One reason for this is high molecular and morphological heterogeneity. The purpose of this study was to develop a method to gain new insights into the connection between morphological changes and underlying molecular patterns. We used artificial intelligence (AI) to analyze the morphology of seven hematoxylin and eosin (H&E)-stained prostatectomy slides from a patient with multi-focal prostate cancer. We also paired the slides with spatially resolved expression for thousands of genes obtained by a novel spatial transcriptomics (ST) technique. As both spaces are highly dimensional, we focused on dimensionality reduction before seeking associations between them. Consequently, we extracted morphological features from H&E images using an ensemble of pre-trained convolutional neural networks and proposed a workflow for dimensionality reduction. To summarize the ST data into genetic profiles, we used a previously proposed factor analysis. We found that the regions were automatically defined, outlined by unsupervised clustering, associated with independent manual annotations, in some cases, finding further relevant subdivisions. The morphological patterns were also correlated with molecular profiles and could predict the spatial variation of individual genes. This novel approach enables flexible unsupervised studies relating morphological and genetic heterogeneity using AI to be carried out.", "doi": "10.3390/cancers13194837", "pmid": "34638322", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "cancers13194837"}, {"db": "pmc", "key": "PMC8507756"}], "notes": [], "created": "2021-11-29T12:38:38.795Z", "modified": "2022-11-02T06:15:40.497Z"}, {"entity": "publication", "iuid": "c20fafa5b31244a9ab60fc632f64d9c3", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c20fafa5b31244a9ab60fc632f64d9c3.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c20fafa5b31244a9ab60fc632f64d9c3"}}, "title": "Machine learning for cell classification and neighborhood analysis in glioma tissue.", "authors": [{"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications.scilifelab.se/researcher/24c62ca6579b4055a95d0dba53722939.json"}}, {"family": "Wik", "given": "Lina", "initials": "L", "orcid": "0000-0001-8406-1828", "researcher": {"href": "https://publications.scilifelab.se/researcher/ae592fc4e7404511a39f79638003a7aa.json"}}, {"family": "Olsson Bontell", "given": "Thomas", "initials": "T"}, {"family": "Wang", "given": "Yuyu", "initials": "Y"}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "\u00d6fverstedt", "given": "Johan", "initials": "J", "orcid": "0000-0003-0253-9037", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ade42e706714d9b8f0b9e7b8b26890d.json"}}, {"family": "Jakola", "given": "Asgeir S", "initials": "AS", "orcid": "0000-0002-2860-9331", "researcher": {"href": "https://publications.scilifelab.se/researcher/f2ebf72073a64dad9f5b16b38754d905.json"}}, {"family": "\u00d6stman", "given": "Arne", "initials": "A", "orcid": "0000-0003-3993-0021", "researcher": {"href": "https://publications.scilifelab.se/researcher/fa382835e9554f5db45fa6cd86f04eab.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2021-06-04", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922"}, "abstract": "Multiplexed and spatially resolved single-cell analyses that intend to study tissue heterogeneity and cell organization invariably face as a first step the challenge of cell classification. Accuracy and reproducibility are important for the downstream process of counting cells, quantifying cell-cell interactions, and extracting information on disease-specific localized cell niches. Novel staining techniques make it possible to visualize and quantify large numbers of cell-specific molecular markers in parallel. However, due to variations in sample handling and artifacts from staining and scanning, cells of the same type may present different marker profiles both within and across samples. We address multiplexed immunofluorescence data from tissue microarrays of low-grade gliomas and present a methodology using two different machine learning architectures and features insensitive to illumination to perform cell classification. The fully automated cell classification provides a measure of confidence for the decision and requires a comparably small annotated data set for training, which can be created using freely available tools. Using the proposed method, we reached an accuracy of 83.1% on cell classification without the need for standardization of samples. Using our confidence measure, cells with low-confidence classifications could be excluded, pushing the classification accuracy to 94.5%. Next, we used the cell classification results to search for cell niches with an unsupervised learning approach based on graph neural networks. We show that the approach can re-detect specialized tissue niches in previously published data, and that our proposed cell classification leads to niche definitions that may be relevant for sub-groups of glioma, if applied to larger data sets.", "doi": "10.1002/cyto.a.24467", "pmid": "34089228", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2021-11-29T12:36:20.429Z", "modified": "2021-11-29T12:36:20.618Z"}, {"entity": "publication", "iuid": "eb542f0c8e904bd687fcec6c03eed0d5", "links": {"self": {"href": "https://publications.scilifelab.se/publication/eb542f0c8e904bd687fcec6c03eed0d5.json"}, "display": {"href": "https://publications.scilifelab.se/publication/eb542f0c8e904bd687fcec6c03eed0d5"}}, "title": "Automated identification of the mouse brain's spatial compartments from in situ sequencing data.", "authors": [{"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications.scilifelab.se/researcher/cca1e7c3e70a4d45aacc3a46d14b9cbe.json"}}, {"family": "Hilscher", "given": "Markus M", "initials": "MM"}, {"family": "Milli", "given": "Giorgia", "initials": "G"}, {"family": "Solorzano", "given": "Leslie", "initials": "L"}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2020-10-19", "journal": {"title": "BMC Biol.", "issn": "1741-7007", "issn-l": "1741-7007", "volume": "18", "issue": "1", "pages": "144"}, "abstract": "Neuroanatomical compartments of the mouse brain are identified and outlined mainly based on manual annotations of samples using features related to tissue and cellular morphology, taking advantage of publicly available reference atlases. However, this task is challenging since sliced tissue sections are rarely perfectly parallel or angled with respect to sections in the reference atlas and organs from different individuals may vary in size and shape and requires manual annotation. With the advent of in situ sequencing technologies and automated approaches, it is now possible to profile the gene expression of targeted genes inside preserved tissue samples and thus spatially map biological processes across anatomical compartments.\r\n\r\nHere, we show how in situ sequencing data combined with dimensionality reduction and clustering can be used to identify spatial compartments that correspond to known anatomical compartments of the brain. We also visualize gradients in gene expression and sharp as well as smooth transitions between different compartments. We apply our method on mouse brain sections and show that a fully unsupervised approach can computationally define anatomical compartments, which are highly reproducible across individuals, using as few as 18 gene markers. We also show that morphological variation does not always follow gene expression, and different spatial compartments can be defined by various cell types with common morphological features but distinct gene expression profiles.\r\n\r\nWe show that spatial gene expression data can be used for unsupervised and unbiased annotations of mouse brain spatial compartments based only on molecular markers, without the need of subjective manual annotations based on tissue and cell morphology or matching reference atlases.", "doi": "10.1186/s12915-020-00874-5", "pmid": "33076915", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics Support for Computational Resources": "Service", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "10.1186/s12915-020-00874-5"}, {"db": "pmc", "key": "PMC7574211"}], "notes": [], "created": "2020-11-30T10:24:03.397Z", "modified": "2024-01-16T13:48:41.550Z"}, {"entity": "publication", "iuid": "c1968e5cd89444e58da6e554cce5605f", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c1968e5cd89444e58da6e554cce5605f.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c1968e5cd89444e58da6e554cce5605f"}}, "title": "Regular use of depot medroxyprogesterone acetate causes thinning of the superficial lining and apical distribution of HIV target cells in the human ectocervix.", "authors": [{"family": "Edfeldt", "given": "Gabriella", "initials": "G"}, {"family": "Lajoie", "given": "Julie", "initials": "J"}, {"family": "R\u00f6hl", "given": "Maria", "initials": "M"}, {"family": "Oyugi", "given": "Julius", "initials": "J"}, {"family": "\u00c5hlberg", "given": "Alexandra", "initials": "A"}, {"family": "Khalilzadeh-Binicy", "given": "Behnaz", "initials": "B"}, {"family": "Bradley", "given": "Frideborg", "initials": "F"}, {"family": "Mack", "given": "Mathias", "initials": "M"}, {"family": "Kimani", "given": "Joshua", "initials": "J"}, {"family": "Omollo", "given": "Kenneth", "initials": "K"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Fowke", "given": "Keith R", "initials": "KR"}, {"family": "Broliden", "given": "Kristina", "initials": "K"}, {"family": "Tjernlund", "given": "Annelie", "initials": "A"}], "type": "journal article", "published": "2020-08-11", "journal": {"title": "J. Infect. Dis.", "issn": "1537-6613", "issn-l": "0022-1899", "volume": null, "issue": null, "pages": null}, "abstract": "The hormonal contraceptive depot medroxyprogesterone acetate (DMPA) may be associated with an increased risk of acquiring human immunodeficiency virus (HIV). We hypothesize that DMPA use influences the ectocervical tissue architecture and HIV target cell localization.\r\n\r\nQuantitative image analysis workflows were developed to assess ectocervical tissue samples collected from DMPA users and control subjects not using hormonal contraception.\r\n\r\nCompared to controls, the DMPA group exhibited a significantly thinner apical ectocervical epithelial layer and a higher proportion of CD4+CCR5+ cells with a more superficial location. This localization corresponded to an area with a non-intact E-cadherin net structure. CD4+Langerin+ cells were also more superficially located in the DMPA group, while fewer in number compared to the controls. Natural plasma progesterone levels did not correlate with any of these parameters, whereas estradiol levels were positively correlated with E-cadherin expression and a more basal location for HIV target cells of the control group.\r\n\r\nDMPA users have a less robust epithelial layer and a more apical distribution of HIV target cells in the human ectocervix, which could confer a higher risk of HIV infection. Our results highlight the importance of assessing intact genital tissue samples to gain insights into HIV susceptibility factors.", "doi": "10.1093/infdis/jiaa514", "pmid": "32780807", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [{"db": "pii", "key": "5891260"}], "notes": [], "created": "2020-11-30T10:24:22.436Z", "modified": "2022-03-29T11:54:23.722Z"}, {"entity": "publication", "iuid": "b22189adeaf145e4a32c95b246088b7b", "links": {"self": {"href": "https://publications.scilifelab.se/publication/b22189adeaf145e4a32c95b246088b7b.json"}, "display": {"href": "https://publications.scilifelab.se/publication/b22189adeaf145e4a32c95b246088b7b"}}, "title": "Artificial intelligence for diagnosis and grading of prostate cancer in biopsies: a population-based, diagnostic study.", "authors": [{"family": "Str\u00f6m", "given": "Peter", "initials": "P"}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K"}, {"family": "Olsson", "given": "Henrik", "initials": "H"}, {"family": "Solorzano", "given": "Leslie", "initials": "L"}, {"family": "Delahunt", "given": "Brett", "initials": "B"}, {"family": "Berney", "given": "Daniel M", "initials": "DM"}, {"family": "Bostwick", "given": "David G", "initials": "DG"}, {"family": "Evans", "given": "Andrew J", "initials": "AJ"}, {"family": "Grignon", "given": "David J", "initials": "DJ"}, {"family": "Humphrey", "given": "Peter A", "initials": "PA"}, {"family": "Iczkowski", "given": "Kenneth A", "initials": "KA"}, {"family": "Kench", "given": "James G", "initials": "JG"}, {"family": "Kristiansen", "given": "Glen", "initials": "G"}, {"family": "van der Kwast", "given": "Theodorus H", "initials": "TH"}, {"family": "Leite", "given": "Katia R M", "initials": "KRM"}, {"family": "McKenney", "given": "Jesse K", "initials": "JK"}, {"family": "Oxley", "given": "Jon", "initials": "J"}, {"family": "Pan", "given": "Chin-Chen", "initials": "C"}, {"family": "Samaratunga", "given": "Hemamali", "initials": "H"}, {"family": "Srigley", "given": "John R", "initials": "JR"}, {"family": "Takahashi", "given": "Hiroyuki", "initials": "H"}, {"family": "Tsuzuki", "given": "Toyonori", "initials": "T"}, {"family": "Varma", "given": "Murali", "initials": "M"}, {"family": "Zhou", "given": "Ming", "initials": "M"}, {"family": "Lindberg", "given": "Johan", "initials": "J"}, {"family": "Lindskog", "given": "Cecilia", "initials": "C"}, {"family": "Ruusuvuori", "given": "Pekka", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Gr\u00f6nberg", "given": "Henrik", "initials": "H"}, {"family": "Rantalainen", "given": "Mattias", "initials": "M"}, {"family": "Egevad", "given": "Lars", "initials": "L"}, {"family": "Eklund", "given": "Martin", "initials": "M"}], "type": "journal article", "published": "2020-02-00", "journal": {"title": "Lancet Oncol", "issn": "1474-5488", "issn-l": null, "volume": "21", "issue": "2", "pages": "222-232"}, "abstract": "An increasing volume of prostate biopsies and a worldwide shortage of urological pathologists puts a strain on pathology departments. Additionally, the high intra-observer and inter-observer variability in grading can result in overtreatment and undertreatment of prostate cancer. To alleviate these problems, we aimed to develop an artificial intelligence (AI) system with clinically acceptable accuracy for prostate cancer detection, localisation, and Gleason grading.\r\n\r\nWe digitised 6682 slides from needle core biopsies from 976 randomly selected participants aged 50-69 in the Swedish prospective and population-based STHLM3 diagnostic study done between May 28, 2012, and Dec 30, 2014 (ISRCTN84445406), and another 271 from 93 men from outside the study. The resulting images were used to train deep neural networks for assessment of prostate biopsies. The networks were evaluated by predicting the presence, extent, and Gleason grade of malignant tissue for an independent test dataset comprising 1631 biopsies from 246 men from STHLM3 and an external validation dataset of 330 biopsies from 73 men. We also evaluated grading performance on 87 biopsies individually graded by 23 experienced urological pathologists from the International Society of Urological Pathology. We assessed discriminatory performance by receiver operating characteristics and tumour extent predictions by correlating predicted cancer length against measurements by the reporting pathologist. We quantified the concordance between grades assigned by the AI system and the expert urological pathologists using Cohen's kappa.\r\n\r\nThe AI achieved an area under the receiver operating characteristics curve of 0\u00b7997 (95% CI 0\u00b7994-0\u00b7999) for distinguishing between benign (n=910) and malignant (n=721) biopsy cores on the independent test dataset and 0\u00b7986 (0\u00b7972-0\u00b7996) on the external validation dataset (benign n=108, malignant n=222). The correlation between cancer length predicted by the AI and assigned by the reporting pathologist was 0\u00b796 (95% CI 0\u00b795-0\u00b797) for the independent test dataset and 0\u00b787 (0\u00b784-0\u00b790) for the external validation dataset. For assigning Gleason grades, the AI achieved a mean pairwise kappa of 0\u00b762, which was within the range of the corresponding values for the expert pathologists (0\u00b760-0\u00b773).\r\n\r\nAn AI system can be trained to detect and grade cancer in prostate needle biopsy samples at a ranking comparable to that of international experts in prostate pathology. Clinical application could reduce pathology workload by reducing the assessment of benign biopsies and by automating the task of measuring cancer length in positive biopsy cores. An AI system with expert-level grading performance might contribute a second opinion, aid in standardising grading, and provide pathology expertise in parts of the world where it does not exist.\r\n\r\nSwedish Research Council, Swedish Cancer Society, Swedish eScience Research Center, EIT Health.", "doi": "10.1016/S1470-2045(19)30738-7", "pmid": "31926806", "labels": {"BioImage Informatics": "Service", "Bioinformatics Support for Computational Resources": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [{"db": "pii", "key": "S1470-2045(19)30738-7"}], "notes": [], "created": "2020-11-30T10:32:29.345Z", "modified": "2024-01-16T13:48:42.995Z"}, {"entity": "publication", "iuid": "906ecb6620684f13bfc9d7701554df7a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/906ecb6620684f13bfc9d7701554df7a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/906ecb6620684f13bfc9d7701554df7a"}}, "title": "Impact of Q-Griffithsin anti-HIV microbicide gel in non-human primates: In situ analyses of epithelial and immune cell markers in rectal mucosa.", "authors": [{"family": "G\u00fcnayd\u0131n", "given": "G\u00f6k\u00e7e", "initials": "G"}, {"family": "Edfeldt", "given": "Gabriella", "initials": "G"}, {"family": "Garber", "given": "David A", "initials": "DA", "orcid": "0000-0003-3608-7104", "researcher": {"href": "https://publications.scilifelab.se/researcher/d63f276e79684aef8661654bc329f226.json"}}, {"family": "Asghar", "given": "Muhammad", "initials": "M"}, {"family": "No\u0205l-Romas", "given": "Laura", "initials": "L"}, {"family": "Burgener", "given": "Adam", "initials": "A"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Wang", "given": "Lin", "initials": "L"}, {"family": "Rohan", "given": "Lisa C", "initials": "LC"}, {"family": "Guenthner", "given": "Patricia", "initials": "P"}, {"family": "Mitchell", "given": "James", "initials": "J"}, {"family": "Matoba", "given": "Nobuyuki", "initials": "N"}, {"family": "McNicholl", "given": "Janet M", "initials": "JM"}, {"family": "Palmer", "given": "Kenneth E", "initials": "KE"}, {"family": "Tjernlund", "given": "Annelie", "initials": "A"}, {"family": "Broliden", "given": "Kristina", "initials": "K"}], "type": "journal article", "published": "2019-12-02", "journal": {"title": "Sci Rep", "issn": "2045-2322", "issn-l": "2045-2322", "volume": "9", "issue": "1", "pages": "18120"}, "abstract": "Natural-product derived lectins can function as potent viral inhibitors with minimal toxicity as shown in vitro and in small animal models. We here assessed the effect of rectal application of an anti-HIV lectin-based microbicide Q-Griffithsin (Q-GRFT) in rectal tissue samples from rhesus macaques. E-cadherin+ cells, CD4+ cells and total mucosal cells were assessed using in situ staining combined with a novel customized digital image analysis platform. Variations in cell numbers between baseline, placebo and Q-GRFT treated samples were analyzed using random intercept linear mixed effect models. The frequencies of rectal E-cadherin+ cells remained stable despite multiple tissue samplings and Q-GRFT gel (0.1%, 0.3% and 1%, respectively) treatment. Whereas single dose application of Q-GRFT did not affect the frequencies of rectal CD4+ cells, multi-dose Q-GRFT caused a small, but significant increase of the frequencies of intra-epithelial CD4+ cells (placebo: median 4%; 1% Q-GRFT: median 7%) and of the CD4+ lamina propria cells (placebo: median 30%; 0.1-1% Q-GRFT: median 36-39%). The resting time between sampling points were further associated with minor changes in the total and CD4+ rectal mucosal cell levels. The results add to general knowledge of in vivo evaluation of anti-HIV microbicide application concerning cellular effects in rectal mucosa.", "doi": "10.1038/s41598-019-54493-4", "pmid": "31792342", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [{"db": "pii", "key": "10.1038/s41598-019-54493-4"}, {"db": "pmc", "key": "PMC6889265"}], "notes": [], "created": "2020-01-08T07:07:49.842Z", "modified": "2022-03-29T11:57:29.767Z"}, {"entity": "publication", "iuid": "fe38a9e085de4033b895eda33e1ecee9", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fe38a9e085de4033b895eda33e1ecee9.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fe38a9e085de4033b895eda33e1ecee9"}}, "title": "Deep Learning in Image Cytometry: A Review.", "authors": [{"family": "Gupta", "given": "Anindya", "initials": "A", "orcid": "0000-0003-3557-4947", "researcher": {"href": "https://publications.scilifelab.se/researcher/f5ba336ef3ab4d819a2f367e0e4f988c.json"}}, {"family": "Harrison", "given": "Philip J", "initials": "PJ", "orcid": "0000-0003-4046-9017", "researcher": {"href": "https://publications.scilifelab.se/researcher/bbabe660124f4d96bcc4f604f61e569e.json"}}, {"family": "Wieslander", "given": "H\u00e5kan", "initials": "H", "orcid": "0000-0002-6289-7285", "researcher": {"href": "https://publications.scilifelab.se/researcher/a271478f5e2c407f9e5f9aceb6b92e10.json"}}, {"family": "Pielawski", "given": "Nicolas", "initials": "N", "orcid": "0000-0001-8182-0091", "researcher": {"href": "https://publications.scilifelab.se/researcher/a8bf24da074a451fa245ff20f07e4b48.json"}}, {"family": "Kartasalo", "given": "Kimmo", "initials": "K", "orcid": "0000-0002-9470-4783", "researcher": {"href": "https://publications.scilifelab.se/researcher/da3da754a0264d538a98d8a85874aec1.json"}}, {"family": "Partel", "given": "Gabriele", "initials": "G", "orcid": "0000-0002-4482-3119", "researcher": {"href": "https://publications.scilifelab.se/researcher/cca1e7c3e70a4d45aacc3a46d14b9cbe.json"}}, {"family": "Solorzano", "given": "Leslie", "initials": "L", "orcid": "0000-0001-8658-6417", "researcher": {"href": "https://publications.scilifelab.se/researcher/24c62ca6579b4055a95d0dba53722939.json"}}, {"family": "Suveer", "given": "Amit", "initials": "A", "orcid": "0000-0002-7779-094X", "researcher": {"href": "https://publications.scilifelab.se/researcher/2079df9478364042935868f40b69b113.json"}}, {"family": "Klemm", "given": "Anna H", "initials": "AH", "orcid": "0000-0002-3466-1320", "researcher": {"href": "https://publications.scilifelab.se/researcher/4ae78afb2a424b0ab70d49871f361d13.json"}}, {"family": "Spjuth", "given": "Ola", "initials": "O", "orcid": "0000-0002-8083-2864", "researcher": {"href": "https://publications.scilifelab.se/researcher/605dbd52684d4e54ae4150a9933abe6e.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2019-04-00", "journal": {"title": "Cytometry A", "issn": "1552-4930", "issn-l": "1552-4922", "volume": "95", "issue": "4", "pages": "366-380"}, "abstract": "Artificial intelligence, deep convolutional neural networks, and deep learning are all niche terms that are increasingly appearing in scientific presentations as well as in the general media. In this review, we focus on deep learning and how it is applied to microscopy image data of cells and tissue samples. Starting with an analogy to neuroscience, we aim to give the reader an overview of the key concepts of neural networks, and an understanding of how deep learning differs from more classical approaches for extracting information from image data. We aim to increase the understanding of these methods, while highlighting considerations regarding input data requirements, computational resources, challenges, and limitations. We do not provide a full manual for applying these methods to your own data, but rather review previously published articles on deep learning in image cytometry, and guide the readers toward further reading on specific networks and methods, including new methods not yet applied to cytometry data. \u00a9 2018 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23701", "pmid": "30565841", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pmc", "key": "PMC6590257"}], "notes": [], "created": "2018-12-30T16:32:19.557Z", "modified": "2023-06-19T12:59:01.277Z"}, {"entity": "publication", "iuid": "a366fe5139384ade916225960fa5d8e1", "links": {"self": {"href": "https://publications.scilifelab.se/publication/a366fe5139384ade916225960fa5d8e1.json"}, "display": {"href": "https://publications.scilifelab.se/publication/a366fe5139384ade916225960fa5d8e1"}}, "title": "Zebrafish larvae as a model system for systematic characterization of drugs and genes in dyslipidemia and atherosclerosis", "authors": [{"family": "Bandaru", "given": "Manoj K", "initials": "MK", "orcid": "0000-0002-5664-6711", "researcher": {"href": "https://publications.scilifelab.se/researcher/024e44747cdd4f5f85c1cf61d3320b09.json"}}, {"family": "Emmanouilidou", "given": "Anastasia", "initials": "A"}, {"family": "Ranefall", "given": "Petter", "initials": "P", "orcid": "0000-0002-6699-4015", "researcher": {"href": "https://publications.scilifelab.se/researcher/4332883c0058421f8dfb85406ec03524.json"}}, {"family": "von der Heyde", "given": "Benedikt", "initials": "B", "orcid": "0000-0002-9889-4027", "researcher": {"href": "https://publications.scilifelab.se/researcher/803c0e0639174a50b59ae597802e824f.json"}}, {"family": "Mazzaferro", "given": "Eugenia", "initials": "E"}, {"family": "Klingstr\u00f6m", "given": "Tiffany", "initials": "T"}, {"family": "Masiero", "given": "Mauro", "initials": "M"}, {"family": "Dethlefsen", "given": "Olga", "initials": "O"}, {"family": "Ledin", "given": "Johan", "initials": "J", "orcid": "0000-0002-7319-7735", "researcher": {"href": "https://publications.scilifelab.se/researcher/92e482abc18c49d881d3bf0132b3fbcd.json"}}, {"family": "Larsson", "given": "Anders", "initials": "A"}, {"family": "Brooke", "given": "Hannah L", "initials": "HL"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ingelsson", "given": "Erik", "initials": "E"}, {"family": "den Hoed", "given": "Marcel", "initials": "M", "orcid": "0000-0001-8081-428X", "researcher": {"href": "https://publications.scilifelab.se/researcher/d712cc087d344b15ab9a7971640acebe.json"}}], "type": "posted-content", "published": "2018-12-20", "journal": {"title": "biorxiv", "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": null}, "abstract": null, "doi": "10.1101/502674", "pmid": null, "labels": {"Bioinformatics Support, Infrastructure and Training": "Service", "Bioinformatics Support and Infrastructure": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [], "notes": [], "created": "2020-01-08T07:55:34.310Z", "modified": "2025-12-18T20:08:39.251Z"}, {"entity": "publication", "iuid": "cf04049f915e40eba55527838d31452e", "links": {"self": {"href": "https://publications.scilifelab.se/publication/cf04049f915e40eba55527838d31452e.json"}, "display": {"href": "https://publications.scilifelab.se/publication/cf04049f915e40eba55527838d31452e"}}, "title": "Image-Based Detection of Patient-Specific Drug-Induced Cell-Cycle Effects in Glioblastoma.", "authors": [{"family": "Matuszewski", "given": "Damian J", "initials": "DJ", "orcid": "0000-0002-6148-5174", "researcher": {"href": "https://publications.scilifelab.se/researcher/0329e24125c3443e99e51435bea6af85.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Krona", "given": "Cecilia", "initials": "C"}, {"family": "Nelander", "given": "Sven", "initials": "S", "orcid": "0000-0003-1758-1262", "researcher": {"href": "https://publications.scilifelab.se/researcher/1d684fc3b26d4741b850790ba0571c96.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM"}], "type": "journal article", "published": "2018-12-00", "journal": {"volume": "23", "issn": "2472-5560", "issue": "10", "pages": "1030-1039", "title": "SLAS DISCOVERY: Advancing Life Sciences R&D", "issn-l": "2472-5552"}, "abstract": "Image-based analysis is an increasingly important tool to characterize the effect of drugs in large-scale chemical screens. Herein, we present image and data analysis methods to investigate population cell-cycle dynamics in patient-derived brain tumor cells. Images of glioblastoma cells grown in multiwell plates were used to extract per-cell descriptors, including nuclear DNA content. We reduced the DNA content data from per-cell descriptors to per-well frequency distributions, which were used to identify compounds affecting cell-cycle phase distribution. We analyzed cells from 15 patient cases representing multiple subtypes of glioblastoma and searched for clusters of cell-cycle phase distributions characterizing similarities in response to 249 compounds at 11 doses. We show that this approach applied in a blind analysis with unlabeled substances identified drugs that are commonly used for treating solid tumors as well as other compounds that are well known for inducing cell-cycle arrest. Redistribution of nuclear DNA content signals is thus a robust metric of cell-cycle arrest in patient-derived glioblastoma cells.", "doi": "10.1177/2472555218791414", "pmid": "30074852", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics Support for Computational Resources": "Service", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "S2472-5552(22)06926-X"}], "notes": [], "created": "2018-10-28T08:23:56.585Z", "modified": "2024-01-16T13:48:45.044Z"}, {"entity": "publication", "iuid": "dc33d64b86074fe5b65c804c58ece569", "links": {"self": {"href": "https://publications.scilifelab.se/publication/dc33d64b86074fe5b65c804c58ece569.json"}, "display": {"href": "https://publications.scilifelab.se/publication/dc33d64b86074fe5b65c804c58ece569"}}, "title": "Human Immunodeficiency Virus-Infected Women Have High Numbers of CD103-CD8+ T Cells Residing Close to the Basal Membrane of the Ectocervical Epithelium.", "authors": [{"family": "Gibbs", "given": "Anna", "initials": "A"}, {"family": "Buggert", "given": "Marcus", "initials": "M"}, {"family": "Edfeldt", "given": "Gabriella", "initials": "G"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "Introini", "given": "Andrea", "initials": "A"}, {"family": "Cheuk", "given": "Stanley", "initials": "S"}, {"family": "Martini", "given": "Elisa", "initials": "E"}, {"family": "Eidsmo", "given": "Liv", "initials": "L"}, {"family": "Ball", "given": "Terry B", "initials": "TB"}, {"family": "Kimani", "given": "Joshua", "initials": "J"}, {"family": "Kaul", "given": "Rupert", "initials": "R"}, {"family": "Karlsson", "given": "Annika C", "initials": "AC"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Broliden", "given": "Kristina", "initials": "K"}, {"family": "Tjernlund", "given": "Annelie", "initials": "A"}], "type": "journal article", "published": "2018-07-02", "journal": {"volume": "218", "issn": "1537-6613", "issue": "3", "pages": "453-465", "title": "J. Infect. Dis.", "issn-l": "0022-1899"}, "abstract": "Genital mucosa is the main portal of entry for various incoming pathogens, including human immunodeficiency virus (HIV), hence it is an important site for host immune defenses. Tissue-resident memory T (TRM) cells defend tissue barriers against infections and are characterized by expression of CD103 and CD69. In this study, we describe the composition of CD8+ TRM cells in the ectocervix of healthy and HIV-infected women.\n\nStudy samples were collected from healthy Swedish and Kenyan HIV-infected and uninfected women. Customized computerized image-based in situ analysis was developed to assess the ectocervical biopsies. Genital mucosa and blood samples were assessed by flow cytometry.\n\nAlthough the ectocervical epithelium of healthy women was populated with bona fide CD8+ TRM cells (CD103+CD69+), women infected with HIV displayed a high frequency of CD103-CD8+ cells residing close to their epithelial basal membrane. Accumulation of CD103-CD8+ cells was associated with chemokine expression in the ectocervix and HIV viral load. CD103+CD8+ and CD103-CD8+ T cells expressed cytotoxic effector molecules in the ectocervical epithelium of healthy and HIV-infected women. In addition, women infected with HIV had decreased frequencies of circulating CD103+CD8+ T cells.\n\nOur data provide insight into the distribution of CD8+ TRM cells in human genital mucosa, a critically important location for immune defense against pathogens, including HIV.", "doi": "10.1093/infdis/jix661", "pmid": "29272532", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "4767924"}], "notes": [], "created": "2017-11-01T11:53:19.090Z", "modified": "2021-07-05T14:18:24.424Z"}, {"entity": "publication", "iuid": "e44a33dd6e314239b6bdc20a6c9a3510", "links": {"self": {"href": "https://publications.scilifelab.se/publication/e44a33dd6e314239b6bdc20a6c9a3510.json"}, "display": {"href": "https://publications.scilifelab.se/publication/e44a33dd6e314239b6bdc20a6c9a3510"}}, "title": "Multiplexed fluorescence microscopy reveals heterogeneity among stromal cells in mouse bone marrow sections", "authors": [{"family": "Holzwarth", "given": "Karolin", "initials": "K"}, {"family": "K\u00f6hler", "given": "Ralf", "initials": "R"}, {"family": "Philipsen", "given": "Lars", "initials": "L"}, {"family": "Tokoyoda", "given": "Koji", "initials": "K"}, {"family": "Ladyhina", "given": "Valeriia", "initials": "V"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Niesner", "given": "Raluca A", "initials": "RA"}, {"family": "Hauser", "given": "Anja E", "initials": "AE"}], "type": "journal-article", "published": "2018-07-00", "journal": {"volume": "93", "issn": "1552-4922", "issue": "9", "pages": "876-888", "title": "Cytometry", "issn-l": "1552-4922"}, "abstract": null, "doi": "10.1002/cyto.a.23526", "pmid": "30107096", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2018-10-28T08:20:40.672Z", "modified": "2021-07-05T14:18:24.438Z"}, {"entity": "publication", "iuid": "de37f86e380d4341abbb8ccd3a16914c", "links": {"self": {"href": "https://publications.scilifelab.se/publication/de37f86e380d4341abbb8ccd3a16914c.json"}, "display": {"href": "https://publications.scilifelab.se/publication/de37f86e380d4341abbb8ccd3a16914c"}}, "title": "Quantitative image analysis of protein expression and colocalisation in skin sections", "authors": [{"family": "Zhang", "given": "Hanqian", "initials": "H"}, {"family": "Ericsson", "given": "Maja", "initials": "M"}, {"family": "Virtanen", "given": "Marie", "initials": "M"}, {"family": "Westr\u00f6m", "given": "Simone", "initials": "S"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Vahlquist", "given": "Anders", "initials": "A"}, {"family": "T\u00f6rm\u00e4", "given": "Hans", "initials": "H"}], "type": "journal-article", "published": "2018-02-00", "journal": {"volume": "27", "issn": "0906-6705", "issue": "2", "pages": "196-199", "title": "Exp Dermatol", "issn-l": "0906-6705"}, "abstract": null, "doi": "10.1111/exd.13457", "pmid": "29094393", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [], "notes": [], "created": "2018-10-28T08:16:17.047Z", "modified": "2021-07-05T14:18:24.431Z"}, {"entity": "publication", "iuid": "38bc75af535b4bd59743a0c42e904430", "links": {"self": {"href": "https://publications.scilifelab.se/publication/38bc75af535b4bd59743a0c42e904430.json"}, "display": {"href": "https://publications.scilifelab.se/publication/38bc75af535b4bd59743a0c42e904430"}}, "title": "Whole Slide Image Registration for the Study of Tumor Heterogeneity", "authors": [{"family": "Solorzano", "given": "Leslie", "initials": "L"}, {"family": "Almeida", "given": "Gabriela M", "initials": "GM"}, {"family": "Mesquita", "given": "B\u00e1rbara", "initials": "B"}, {"family": "Martins", "given": "Diana", "initials": "D"}, {"family": "Oliveira", "given": "Carla", "initials": "C"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "book-chapter", "published": "2018-00-00", "journal": {"volume": null, "issn": "0302-9743", "issue": null, "pages": "95-102", "title": null, "issn-l": null}, "abstract": null, "doi": "10.1007/978-3-030-00949-6_12", "pmid": null, "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2018-10-28T08:23:01.321Z", "modified": "2021-07-05T14:18:24.331Z"}, {"entity": "publication", "iuid": "793737c7164b487eb6f97d8b1b3b984a", "links": {"self": {"href": "https://publications.scilifelab.se/publication/793737c7164b487eb6f97d8b1b3b984a.json"}, "display": {"href": "https://publications.scilifelab.se/publication/793737c7164b487eb6f97d8b1b3b984a"}}, "title": "Automated Training of Deep Convolutional Neural Networks for Cell Segmentation", "authors": [{"family": "Sadanandan", "given": "Sajith Kecheril", "initials": "SK"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "Le Guyader", "given": "Sylvie", "initials": "S"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal-article", "published": "2017-12-00", "journal": {"volume": "7", "issn": "2045-2322", "issue": "1", "pages": null, "title": "Sci Rep", "issn-l": "2045-2322"}, "abstract": null, "doi": "10.1038/s41598-017-07599-6", "pmid": "28798336", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "cb.uu.se", "description": "Images and code", "key": "http://www.cb.uu.se/~carolina/timelapse_data/"}, {"db": "data.broadinstitute.org", "description": "Images", "key": "https://data.broadinstitute.org/bbbc/BBBC022/"}], "notes": [], "created": "2017-11-01T08:05:25.830Z", "modified": "2021-07-05T14:18:24.353Z"}, {"entity": "publication", "iuid": "80c5501a15564f2eb10243009cd39671", "links": {"self": {"href": "https://publications.scilifelab.se/publication/80c5501a15564f2eb10243009cd39671.json"}, "display": {"href": "https://publications.scilifelab.se/publication/80c5501a15564f2eb10243009cd39671"}}, "title": "A comprehensive structural, biochemical and biological profiling of the human NUDIX hydrolase family", "authors": [{"family": "Carreras-Puigvert", "given": "Jordi", "initials": "J"}, {"family": "Zitnik", "given": "Marinka", "initials": "M"}, {"family": "Jemth", "given": "Ann Sofie", "initials": "AS"}, {"family": "Carter", "given": "Megan", "initials": "M"}, {"family": "Unterlass", "given": "Judith E", "initials": "JE"}, {"family": "Hallstr\u00f6m", "given": "Bj\u00f6rn", "initials": "B"}, {"family": "Loseva", "given": "Olga", "initials": "O"}, {"family": "Karem", "given": "Zhir", "initials": "Z"}, {"family": "Calder\u00f3n-Monta\u00f1o", "given": "Jos\u00e9 Manuel", "initials": "JM"}, {"family": "Lindskog", "given": "Cecilia", "initials": "C"}, {"family": "Edqvist", "given": "Per Henrik", "initials": "PH"}, {"family": "Matuszewski", "given": "Damian J", "initials": "DJ"}, {"family": "Ait Blal", "given": "Hammou", "initials": "H"}, {"family": "Berntsson", "given": "Ronnie P A", "initials": "RPA"}, {"family": "H\u00e4ggblad", "given": "Maria", "initials": "M"}, {"family": "Martens", "given": "Ulf", "initials": "U"}, {"family": "Studham", "given": "Matthew", "initials": "M"}, {"family": "Lundgren", "given": "Bo", "initials": "B"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sonnhammer", "given": "Erik L L", "initials": "ELL"}, {"family": "Lundberg", "given": "Emma", "initials": "E", "orcid": "0000-0001-7034-0850", "researcher": {"href": "https://publications.scilifelab.se/researcher/1ffe6259ceb540f385861b5ae52b3055.json"}}, {"family": "Stenmark", "given": "P\u00e5l", "initials": "P"}, {"family": "Zupan", "given": "Blaz", "initials": "B"}, {"family": "Helleday", "given": "Thomas", "initials": "T", "orcid": "0000-0002-7384-092X", "researcher": {"href": "https://publications.scilifelab.se/researcher/3d7256c271ea4adea404d4ff355f804e.json"}}], "type": "journal-article", "published": "2017-12-00", "journal": {"volume": "8", "issn": "2041-1723", "issue": "1", "pages": null, "title": "Nat Commun", "issn-l": "2041-1723"}, "abstract": null, "doi": "10.1038/s41467-017-01642-w", "pmid": "29142246", "labels": {"Protein Science Facility (PSF)": "Service", "BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative", "Drug Discovery and Development": "Collaborative"}, "xrefs": [], "notes": "Biochemical and Cellular Screening", "created": "2017-11-16T10:37:23.396Z", "modified": "2025-10-17T13:05:08.653Z"}, {"entity": "publication", "iuid": "18346d9935a9491282d59dfb6b4cd277", "links": {"self": {"href": "https://publications.scilifelab.se/publication/18346d9935a9491282d59dfb6b4cd277.json"}, "display": {"href": "https://publications.scilifelab.se/publication/18346d9935a9491282d59dfb6b4cd277"}}, "title": "A short feature vector for image matching: The Log-Polar Magnitude feature descriptor.", "authors": [{"family": "Matuszewski", "given": "Damian J", "initials": "DJ", "orcid": "0000-0002-6148-5174", "researcher": {"href": "https://publications.scilifelab.se/researcher/0329e24125c3443e99e51435bea6af85.json"}}, {"family": "Hast", "given": "Anders", "initials": "A"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2017-11-30", "journal": {"volume": "12", "issn": "1932-6203", "issue": "11", "pages": "e0188496", "title": "PLoS ONE", "issn-l": "1932-6203"}, "abstract": "The choice of an optimal feature detector-descriptor combination for image matching often depends on the application and the image type. In this paper, we propose the Log-Polar Magnitude feature descriptor-a rotation, scale, and illumination invariant descriptor that achieves comparable performance to SIFT on a large variety of image registration problems but with much shorter feature vectors. The descriptor is based on the Log-Polar Transform followed by a Fourier Transform and selection of the magnitude spectrum components. Selecting different frequency components allows optimizing for image patterns specific for a particular application. In addition, by relying only on coordinates of the found features and (optionally) feature sizes our descriptor is completely detector independent. We propose 48- or 56-long feature vectors that potentially can be shortened even further depending on the application. Shorter feature vectors result in better memory usage and faster matching. This combined with the fact that the descriptor does not require a time-consuming feature orientation estimation (the rotation invariance is achieved solely by using the magnitude spectrum of the Log-Polar Transform) makes it particularly attractive to applications with limited hardware capacity. Evaluation is performed on the standard Oxford dataset and two different microscopy datasets; one with fluorescence and one with transmission electron microscopy images. Our method performs better than SURF and comparable to SIFT on the Oxford dataset, and better than SIFT on both microscopy datasets indicating that it is particularly useful in applications with microscopy images.", "doi": "10.1371/journal.pone.0188496", "pmid": "29190737", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "PONE-D-17-08232"}, {"db": "pmc", "key": "PMC5708636"}], "notes": [], "created": "2018-10-28T08:15:53.022Z", "modified": "2023-06-19T12:59:58.028Z"}, {"entity": "publication", "iuid": "a29d11d5638d409ebe3b76aee7ec77b3", "links": {"self": {"href": "https://publications.scilifelab.se/publication/a29d11d5638d409ebe3b76aee7ec77b3.json"}, "display": {"href": "https://publications.scilifelab.se/publication/a29d11d5638d409ebe3b76aee7ec77b3"}}, "title": "Highly HIV-exposed HIV uninfected Kenyan female sex workers display a thick ectocervical epithelium", "authors": [{"family": "R\u00f6hl", "given": "Maria", "initials": "M"}, {"family": "Lajoie", "given": "Julie", "initials": "J"}, {"family": "Tjernlund", "given": "Annelie", "initials": "A"}, {"family": "Edfeldt", "given": "Gabriella", "initials": "G", "orcid": "0000-0003-0366-5588", "researcher": {"href": "https://publications.scilifelab.se/researcher/6538b950bbd440e3aa32435d23c98074.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Boily-Larouche", "given": "Genevieve", "initials": "G"}, {"family": "Cheruiyot", "given": "Julianna", "initials": "J"}, {"family": "Kimani", "given": "Makubo", "initials": "M"}, {"family": "Kimani", "given": "Joshua", "initials": "J"}, {"family": "Oyugi", "given": "Julius", "initials": "J"}, {"family": "Fowke", "given": "Keith R.", "initials": "KR"}, {"family": "Broliden", "given": "Kristina", "initials": "K", "orcid": "0000-0003-2224-7664", "researcher": {"href": "https://publications.scilifelab.se/researcher/95346da4e5984d48bbb50032797155e5.json"}}], "type": null, "published": "2017-11-01", "journal": {"volume": null, "issn": null, "issue": null, "pages": null, "title": "HIV&Hepatitis Nordic Conference, Stockholm, 2017-09-27-29", "issn-l": null}, "abstract": "Background\r\nThe female genital tract is a critical site of HIV acquisition and a number of genetic and immunological correlates of relative resistance against infection have been described in the ectocervical mucosa. We hypothesize that a thick epithelium, a high concentration of epithelial junction proteins, together with a low concentration of HIV target cells (CCR5+CD4+T cells and dendritic cells) at a distant location is a beneficial combination that hinders sexual acquisition of HIV.\r\n\r\nMethods\r\nEctocervical biopsies were collected from female sex workers from Nairobi, Kenya, representing highly HIV-exposed but HIV seronegative who had been involved in sex work for 7 years or more (HESN) (n=29), HIV-infected (n=11), and uninfected individuals who were new to sex work (3 years or less) (n=39). Digital image analysis of immunofluorescent staining was used to identify genital mucosal factors affecting HIV susceptibility by characterizing the thickness and integrity as well as the spatial distribution of HIV target cells in the cervical epithelium.\r\n\r\nResults\r\nPreliminary results indicate that the HESN group display significantly thicker epithelium than the HIV+ group, and significantly lower numbers of potential HIV target cells than both the HIV+ group and those who were new to sex work.\r\n\r\nConclusion\r\nA deeper insight into what mucosal factors are affecting HIV susceptibility is of major importance to prevent sexual HIV transmission, and we hope to contribute to this needed knowledge.", "doi": null, "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T11:47:33.003Z", "modified": "2025-11-17T09:52:46.049Z"}, {"entity": "publication", "iuid": "d4ce1c0ed7c94ee7a6109cab0534cf0e", "links": {"self": {"href": "https://publications.scilifelab.se/publication/d4ce1c0ed7c94ee7a6109cab0534cf0e.json"}, "display": {"href": "https://publications.scilifelab.se/publication/d4ce1c0ed7c94ee7a6109cab0534cf0e"}}, "title": "Quantitative high-content/high-throughput microscopy analysis of lipid droplets in subject-specific adipogenesis models.", "authors": [{"family": "Bombrun", "given": "Maxime", "initials": "M"}, {"family": "Gao", "given": "Hui", "initials": "H"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "Mejhert", "given": "Niklas", "initials": "N"}, {"family": "Arner", "given": "Peter", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2017-11-00", "journal": {"volume": "91", "issn": "1552-4930", "issue": "11", "pages": "1068-1077", "title": "Cytometry A", "issn-l": "1552-4922"}, "abstract": "Neutral lipids packed in lipid droplets (LDs) are essential as a source of fuel for organisms, and specialized storing cells, the adipocytes, provide a buffer for energy variations. Many modern-society-disorders are connected with excess accumulation or deficiency of LDs in adipose tissue. Intracellular LD number and size distribution reflect the tissue conditions, while the associated mechanisms and genes rs are still poorly understood. Large-scale genetic screens using human in vitro differentiated primary adipocytes require cell samples donated from many patients. The heterogeneity appearing between donors highlighted the need for high-throughput methods robust to individual variations. Previous image analysis algorithms failed to handle individual LDs, but focused on averages, hiding population heterogeneity. We present a new high-content analysis (HCA) technique for analysis of fat cell metabolism using data from a large-scale RNAi screen including images of more than 500 k in vitro differentiated adipocytes from three donors. The RNAi-based suppression of Perilipin 1 (PLIN1), a protein involved in the adipocyte lipid metabolism, served as a positive control, while cells treated with randomized RNA served as negative controls. We validate our segmentation by comparing our results to those of previously published methods: We also evaluate the discriminative power of different morphological features describing LD size distribution. Classification of cells as containing few large or many small LDs followed by calculating the percentage of cells in each class proved to discriminate the positive PLIN1-suppressed phenotype from the untreated negative control with an area under the receiver operating characteristic curve of 0.98. The results suggest that this HCA method offers improved segmentation and classification accuracy, and can, thus, be utilized to quantify changes in LD metabolism in response to treatment in many cell models relevant to a variety of diseases. \u00a9 2017 International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.23265", "pmid": "29031005", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T07:51:39.316Z", "modified": "2021-07-05T14:18:24.410Z"}, {"entity": "publication", "iuid": "483c2c8715ac4b629743df2a4398fb2e", "links": {"self": {"href": "https://publications.scilifelab.se/publication/483c2c8715ac4b629743df2a4398fb2e.json"}, "display": {"href": "https://publications.scilifelab.se/publication/483c2c8715ac4b629743df2a4398fb2e"}}, "title": "Zebrafish larvae as a model system for high-throughput, image-based genetic screens in cardiometabolic diseases", "authors": [{"family": "den Hoed", "given": "Marcel", "initials": "M", "orcid": "0000-0001-8081-428X", "researcher": {"href": "https://publications.scilifelab.se/researcher/d712cc087d344b15ab9a7971640acebe.json"}}, {"family": "Emmanouilidou", "given": "Anastasia", "initials": "A"}, {"family": "Bandaru", "given": "Manoj", "initials": "M", "orcid": "0000-0002-5664-6711", "researcher": {"href": "https://publications.scilifelab.se/researcher/024e44747cdd4f5f85c1cf61d3320b09.json"}}, {"family": "von der Heyde", "given": "Benedikt", "initials": "B", "orcid": "0000-0002-9889-4027", "researcher": {"href": "https://publications.scilifelab.se/researcher/803c0e0639174a50b59ae597802e824f.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ranefall", "given": "Petter", "initials": "P", "orcid": "0000-0002-6699-4015", "researcher": {"href": "https://publications.scilifelab.se/researcher/4332883c0058421f8dfb85406ec03524.json"}}, {"family": "Allalou", "given": "Amin", "initials": "A", "orcid": "0000-0003-4028-8443", "researcher": {"href": "https://publications.scilifelab.se/researcher/98fffa8e99254fb597bf07dea61d8e37.json"}}, {"family": "Larsson", "given": "Anders", "initials": "A"}, {"family": "Ingelsson", "given": "Erik", "initials": "E"}], "type": null, "published": "2017-10-17", "journal": {"volume": null, "issn": null, "issue": null, "pages": null, "title": "ASHG 2017 Annual Meeting - The American Society of Human Genetics", "issn-l": null}, "abstract": "Background: Genome-wide association, exome array and whole-exome sequencing efforts have identified hundreds of loci that are robustly associated with the risk of cardiometabolic diseases and risk factors. With few exceptions, the causal genes through which these loci influence the risk of disease remain uncharacterized. While in silico genomic annotation using results from e.g. the ENCODE and RoadMap Epigenomics projects provides valuable insights, novel model systems that enable systematic, in vivo characterization of candidate genes are desirable.\r\n\r\nMethods: My group has developed and validated zebrafish model systems that make optimal use of: 1) the zebrafish\u2019 well-annotated genome, with orthologues of \u226571.4% of human genes; 2) recent developments in multiplex CRISPR-Cas9-based mutagenesis; 3) advances in automated positioning of non-embedded zebrafish larvae; 4) fluorescent transgenes and dyes; and 5) custom-written image-quantification pipelines.\r\n\r\nResults: Five days of overfeeding, dietary cholesterol supplementation and/or exposure to glucose induce atherogenic and insulin resistant phenotypes (higher/more whole-body LDL cholesterol (LDLc) levels; vascular foam cell formation and inflammation; \u03b2-cell number and volume; subcutaneous and hepatic accumulation of fat) that can largely be prevented by concomitant treatment with lipid-lowering or diabetes medication (N>4000). Proof-of-principle studies show that each additional mutated allele in the zebrafish\u2019 orthologues of APOE (apoea, apoeb) results in higher LDLc levels and more vascular foam cell formation and inflammation (N~384). In line with recent results in humans, treatment with LDLc-lowering drugs (N~400) and mutations in pcsk9 (N~384) both result in higher whole-body glucose levels. Finally, characterization of candidate genes in loci identified in a recent GWAS for heart rate variability helped identify genes that influence early-stage cardiac development, cardiac rate, and/or cardiac rhythm.\r\n\r\nConclusions: Systematic, largely image-based characterization of candidate genes for cardiometabolic traits in zebrafish model systems will increase our understanding of human disease, and will likely identify novel targets that can be translated into efficient therapeutics. In addition, undesirable side effects can be quantified in vivo at an early stage, thereby preventing costly and time-consuming experiments for targets that would otherwise likely fail on the road towards clinical trials.", "doi": null, "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-10T14:47:25.028Z", "modified": "2025-11-17T09:52:37.258Z"}, {"entity": "publication", "iuid": "fbf2fa0eb8664f2399794717d866aeb7", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fbf2fa0eb8664f2399794717d866aeb7.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fbf2fa0eb8664f2399794717d866aeb7"}}, "title": "Large-scale validation of zebrafish larvae as a model system for genetic screens in dyslipidaemia, atherosclerosis and coronary artery disease", "authors": [{"family": "Bandaru", "given": "Manoj", "initials": "M", "orcid": "0000-0002-5664-6711", "researcher": {"href": "https://publications.scilifelab.se/researcher/024e44747cdd4f5f85c1cf61d3320b09.json"}}, {"family": "Emmanouilidou", "given": "Anastasia", "initials": "A"}, {"family": "Ranefall", "given": "Petter", "initials": "P", "orcid": "0000-0002-6699-4015", "researcher": {"href": "https://publications.scilifelab.se/researcher/4332883c0058421f8dfb85406ec03524.json"}}, {"family": "von der Heyde", "given": "Benedikt", "initials": "B", "orcid": "0000-0002-9889-4027", "researcher": {"href": "https://publications.scilifelab.se/researcher/803c0e0639174a50b59ae597802e824f.json"}}, {"family": "Klingstr\u00f6m", "given": "Tiffany", "initials": "T"}, {"family": "Ledin", "given": "Johan", "initials": "J", "orcid": "0000-0002-7319-7735", "researcher": {"href": "https://publications.scilifelab.se/researcher/92e482abc18c49d881d3bf0132b3fbcd.json"}}, {"family": "Larsson", "given": "Anders", "initials": "A"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Ingelsson", "given": "Erik", "initials": "E"}, {"family": "den Hoed", "given": "Marcel", "initials": "M", "orcid": "0000-0001-8081-428X", "researcher": {"href": "https://publications.scilifelab.se/researcher/d712cc087d344b15ab9a7971640acebe.json"}}], "type": null, "published": "2017-10-17", "journal": {"volume": null, "issn": null, "issue": null, "pages": null, "title": "ASHG 2017 Annual Meeting - The American Society of Human Genetics", "issn-l": null}, "abstract": "Background: Genome-wide association studies have identified 77 loci that are robustly associated with coronary artery disease (CAD). In all but a few of these loci the causal genes and mechanisms remain unknown. Results from small-scale studies suggest that zebrafish larvae represent a promising model system for genetic screens in dyslipidemia, early-stage atherosclerosis and CAD. We aim to confirm or refute these results in a large-scale study, expand the phenotypic pipeline, and increase the throughput.\r\n\r\nMethods: At the core of our setup is an automated positioning and imaging system that allows visualization and quantification of atherogenic traits in ~100 zebrafish larvae per day at 10 days post-fertilization, by making use of fluorescent transgenes and dyes. We used a three-tiered approach to validate the zebrafish model system: 1) a dietary intervention to examine the effect of overfeeding and cholesterol supplementation (N=2193); 2) a treatment regime with atorvastatin and ezetimibe (N=956); and 3) a genetic screen for zebrafish orthologues of LDLR, PCSK9, APOB and APOE using a multiplex CRISPR-Cas9 approach (N=2x384). After imaging, whole-body lipid and glucose levels were assessed using enzymatic assays, and CRISPR-Cas9 target sites were sequenced on a MiSeq.\r\n\r\nResults: Overfeeding and cholesterol supplementation have independent pro-atherogenic effects, including elevated total cholesterol and triglyceride levels, more vascular deposition of lipids and oxidized LDLc, and more co-localization of lipids with macrophages and neutrophils. Treatment with atorvastatin and ezetimibe results in lower whole-body total cholesterol, LDLc and triglyceride levels, as well as in less vascular lipid deposition and less co-localization of lipids and macrophages. Finally, mutations in APOE orthologues result in higher whole-body LDLc levels and more co-localization of lipids with macrophages or neutrophils compared with wildtypes. Mutations in APOB orthologues tend to result in higher LDLc levels, more vascular lipid deposition, and more co-localizing lipids and neutrophils. Treatment with lipid lowering drugs and mutations in pcsk9 both result in higher whole-body glucose levels. Data from all larvae combined show that atherosclerosis in 10-day-old zebrafish larvae is mainly driven by higher triglyceride but not LDLc levels.\r\n\r\nConclusion: Zebrafish larvae can be used to systematically identify and characterize causal genes for CAD.", "doi": null, "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-10T08:53:10.829Z", "modified": "2025-11-17T09:58:04.394Z"}, {"entity": "publication", "iuid": "0cd49b807b98485fb62dcca6a680aa25", "links": {"self": {"href": "https://publications.scilifelab.se/publication/0cd49b807b98485fb62dcca6a680aa25.json"}, "display": {"href": "https://publications.scilifelab.se/publication/0cd49b807b98485fb62dcca6a680aa25"}}, "title": "Deep Convolutional Neural Networks for Detecting Cellular Changes Due to Malignancy", "authors": [{"family": "Forslid", "given": "Gustav", "initials": "G"}, {"family": "Wieslander", "given": "Hakan", "initials": "H"}, {"family": "Bengtsson", "given": "Ewert", "initials": "E"}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Hirsch", "given": "Jan Michael", "initials": "JM"}, {"family": "Stark", "given": "Christina Runow", "initials": "CR"}, {"family": "Sadanandan", "given": "Sajith Kecheril", "initials": "SK"}], "type": "proceedings-article", "published": "2017-10-00", "journal": {"title": "The IEEE International Conference on Computer Vision (ICCV), 2017", "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": null}, "abstract": "Discovering cancer at an early stage is an effective way to increase the chance of survival. However, since most screening processes are done manually it is time inef\ufb01cient and thus a costly process. One way of automizing the screening process could be to classify cells using Convolutional Neural Networks. Convolutional Neural Networks have been proven to be accurate for image classi\ufb01cation tasks. Two datasets containing oral cells and two datasets containing cervical cells were used. For the cervical cancer dataset the cells were classi\ufb01ed by medical experts as normal or abnormal. For the oral cell dataset we only used the diagnosis of the patient. All cells obtained from a patient with malignancy were thus considered malignant even though most of them looked normal. The performance was evaluated for two different network architectures, ResNet and VGG. For the oral datasets the accuracy varied between 78-82% correctly classi\ufb01ed cells depending on the dataset and network. For the cervical datasets the accuracy varied between 84-86% correctly classi\ufb01ed cells depending on the dataset and network. The results indicate a high potential for detecting abnormalities in oral cavity and in uterine cervix. ResNet was shown to be the preferable network, with a higher accuracy and a smaller standard deviation.", "doi": "10.1109/iccvw.2017.18", "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T08:42:44.190Z", "modified": "2023-06-02T10:28:56.445Z"}, {"entity": "publication", "iuid": "fe676690ff9d483394c2d54033ee1aac", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fe676690ff9d483394c2d54033ee1aac.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fe676690ff9d483394c2d54033ee1aac"}}, "title": "Spheroid Segmentation Using Multiscale Deep Adversarial Networks", "authors": [{"family": "Sadanandan", "given": "Sajith Kecheril", "initials": "SK"}, {"family": "Karlsson", "given": "Johan", "initials": "J"}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "proceedings-article", "published": "2017-10-00", "journal": {"title": "The IEEE International Conference on Computer Vision (ICCV), 2017", "issn": null, "issn-l": null, "volume": null, "issue": null, "pages": "36-41"}, "abstract": "In this work, we segment spheroids with different sizes, shapes, and illumination conditions from bright-field microscopy images. To segment the spheroids we create a novel multiscale deep adversarial network with different deep feature extraction layers at different scales. We show that linearly increasing the adversarial loss contribution results in a stable segmentation algorithm for our dataset. We qualitatively and quantitatively compare the performance of our deep adversarial network with two other networks without adversarial losses. We show that our deep adversarial network performs better than the other two networks at segmenting the spheroids from our 2D bright-field microscopy images.", "doi": "10.1109/iccvw.2017.11", "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T08:33:16.067Z", "modified": "2023-06-02T10:28:50.765Z"}, {"entity": "publication", "iuid": "d66cd6742ac545c7a7c30d4f264ec1b2", "links": {"self": {"href": "https://publications.scilifelab.se/publication/d66cd6742ac545c7a7c30d4f264ec1b2.json"}, "display": {"href": "https://publications.scilifelab.se/publication/d66cd6742ac545c7a7c30d4f264ec1b2"}}, "title": "Objective automated quantification of fluorescence signal in histological sections of rat lens", "authors": [{"family": "Talebizadeh", "given": "Nooshin", "initials": "N"}, {"family": "Hagstr\u00f6m", "given": "Nanna Zhou", "initials": "NZ"}, {"family": "Yu", "given": "Zhaohua", "initials": "Z"}, {"family": "Kronschl\u00e4ger", "given": "Martin", "initials": "M"}, {"family": "S\u00f6derberg", "given": "Per", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal-article", "published": "2017-08-00", "journal": {"volume": "91", "issn": "1552-4922", "issue": "8", "pages": "815-821", "title": "Cytometry", "issn-l": "1552-4922"}, "abstract": null, "doi": "10.1002/cyto.a.23131", "pmid": "28494118", "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T08:23:18.937Z", "modified": "2021-07-05T14:18:24.417Z"}, {"entity": "publication", "iuid": "0be663b8d3934de29814d3954f39725e", "links": {"self": {"href": "https://publications.scilifelab.se/publication/0be663b8d3934de29814d3954f39725e.json"}, "display": {"href": "https://publications.scilifelab.se/publication/0be663b8d3934de29814d3954f39725e"}}, "title": "Deep Fish: Deep Learning\u2013Based Classification of Zebrafish Deformation for High-Throughput Screening", "authors": [{"family": "Ishaq", "given": "Omer", "initials": "O"}, {"family": "Sadanandan", "given": "Sajith Kecheril", "initials": "SK"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal-article", "published": "2017-01-00", "journal": {"volume": "22", "issn": "2472-5552", "issue": "1", "pages": "102-107", "title": "SLAS DISCOVERY: Advancing Life Sciences R&D", "issn-l": null}, "abstract": null, "doi": "10.1177/1087057116667894", "pmid": "27613194", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2017-11-01T08:23:55.870Z", "modified": "2021-07-05T14:18:24.278Z"}, {"entity": "publication", "iuid": "9784434acb164d7f840bedaa69e012f1", "links": {"self": {"href": "https://publications.scilifelab.se/publication/9784434acb164d7f840bedaa69e012f1.json"}, "display": {"href": "https://publications.scilifelab.se/publication/9784434acb164d7f840bedaa69e012f1"}}, "title": "Decoding Gene Expression in 2D and 3D", "authors": [{"family": "Bombrun", "given": "Maxime", "initials": "M"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "Lindblad", "given": "Joakim", "initials": "J"}, {"family": "Allalou", "given": "Amin", "initials": "A"}, {"family": "Partel", "given": "Gabriele", "initials": "G"}, {"family": "Solorzano", "given": "Leslie", "initials": "L"}, {"family": "Qian", "given": "Xiaoyan", "initials": "X"}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "book-chapter", "published": "2017-00-00", "journal": {"volume": null, "issn": "0302-9743", "issue": null, "pages": "257-268", "title": "Scandinavian Conference on Image Analysis 2017", "issn-l": null}, "abstract": null, "doi": "10.1007/978-3-319-59129-2_22", "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T08:06:23.725Z", "modified": "2021-07-07T13:54:46.111Z"}, {"entity": "publication", "iuid": "a30dbedd94c94e36a8ea47ce308374d4", "links": {"self": {"href": "https://publications.scilifelab.se/publication/a30dbedd94c94e36a8ea47ce308374d4.json"}, "display": {"href": "https://publications.scilifelab.se/publication/a30dbedd94c94e36a8ea47ce308374d4"}}, "title": "Differential Neuroprotective Effects of Interleukin-1 Receptor Antagonist on Spinal Cord Neurons after Excitotoxic Injury", "authors": [{"family": "Schizas", "given": "Nikos", "initials": "N"}, {"family": "Perry", "given": "Sharn", "initials": "S"}, {"family": "Andersson", "given": "Brittmarie", "initials": "B"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Kullander", "given": "Klas", "initials": "K"}, {"family": "Hailer", "given": "Nils P", "initials": "NP"}], "type": "journal-article", "published": "2017-00-00", "journal": {"volume": "24", "issn": "1021-7401", "issue": "4-5", "pages": "220-230", "title": "Neuroimmunomodulation", "issn-l": null}, "abstract": null, "doi": "10.1159/000484607", "pmid": "29393213", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [], "notes": [], "created": "2018-10-28T08:14:36.460Z", "modified": "2021-07-05T14:18:24.389Z"}, {"entity": "publication", "iuid": "2625e25ccebf40388bfc4b5f0d0a6100", "links": {"self": {"href": "https://publications.scilifelab.se/publication/2625e25ccebf40388bfc4b5f0d0a6100.json"}, "display": {"href": "https://publications.scilifelab.se/publication/2625e25ccebf40388bfc4b5f0d0a6100"}}, "title": "Analysis of the Distribution of CD103 on CD8 T Cells in Blood and Genital Mucosa of HIVinfected Female Sex Workers", "authors": [{"family": "Gibbs", "given": "Anna", "initials": "A"}, {"family": "Buggert", "given": "Marcus", "initials": "M", "orcid": "0000-0003-0633-1719", "researcher": {"href": "https://publications.scilifelab.se/researcher/9a54e4b5136642eeafa77ac5118a0c81.json"}}, {"family": "Ranefall", "given": "Petter", "initials": "P", "orcid": "0000-0002-6699-4015", "researcher": {"href": "https://publications.scilifelab.se/researcher/4332883c0058421f8dfb85406ec03524.json"}}, {"family": "Introini", "given": "Andrea", "initials": "A"}, {"family": "Cheuk", "given": "Stanley", "initials": "S"}, {"family": "Eidsmo", "given": "Liv", "initials": "L", "orcid": "0000-0001-9237-8374", "researcher": {"href": "https://publications.scilifelab.se/researcher/57e083936a8144a19f0b2eb14dab6898.json"}}, {"family": "Hirbod", "given": "Taha", "initials": "T"}, {"family": "Ball", "given": "TerryB.", "initials": "T"}, {"family": "Kimani", "given": "Joshua", "initials": "J"}, {"family": "Kaul", "given": "Rupert", "initials": "R"}, {"family": "Karlsson", "given": "Annika C.", "initials": "AC"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Broliden", "given": "Kristina", "initials": "K", "orcid": "0000-0003-2224-7664", "researcher": {"href": "https://publications.scilifelab.se/researcher/95346da4e5984d48bbb50032797155e5.json"}}, {"family": "Tjernlund", "given": "Annelie", "initials": "A"}], "type": null, "published": "2016-10-17", "journal": {"volume": null, "issn": null, "issue": null, "pages": null, "title": "Conference on HIV Research for Prevention (HIV R4P), 2016, October 17\u201320, Chicago", "issn-l": null}, "abstract": "Background: Tissue resident memory (TRM) cells are characterized by the expression of several markers including \u03b1E(CD103)\u03b27 integrin and by their preferential localization in the epithelium of mucosal surfaces. However, little is known about the presence of TRM cells in the human female reproductive tract (FRT), and their eventual role in protecting the host against HIV infection therein. In this study, we compared the distribution of CD103, as a surrogate marker of tissue-residency, on CD8+ T cells between the FRT and blood of HIV-infected and uninfected women. \r\n\r\nMethods: Blood and ectocervical tissue biopsies were collected from HIV-seropositive (n = 19) and HIV-seronegative (n = 17) Kenyan female sex workers as well as HIV-seronegative lowerrisk women (n = 21). Flow cytometry was used to assess the phenotype of circulating CD103+ CD8+ T cells, while in situ staining with image analysis were performed to enumerate CD8+ CD103+ cells in ectocervical biopsies. \r\n\r\nResults: The HIV-infected women displayed a significantly lower proportion of circulating CD103+cells within CD8+ T cells as compared to uninfected women. Circulating CD103+CD8+ T cells from the HIV-infected women were highly activated and enriched within the effector memory pool. Similar to blood, the proportion of cervical CD103+ cells among CD8+ cells was significantly lower in the HIV-infected versus uninfected women, even though the absolute count of these cells was increased in the HIV-infected women. \r\n\r\nConclusions: Our data suggests that CD8+ T cells with the potential of residing within mucosal sites in virtue of their CD103 expression may be actively recruited to/or expanded in the FRT of chronically HIV-infected sex workers. This data poses the basis for further analysis of the phenotype and function of CD103+ CD8+ T cells residing in the FRT as TRM, and their eventual changes in the course of HIV infection.", "doi": null, "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T12:02:48.993Z", "modified": "2025-11-17T09:52:26.023Z"}, {"entity": "publication", "iuid": "1f83014b47ad46809069fc41391c42bb", "links": {"self": {"href": "https://publications.scilifelab.se/publication/1f83014b47ad46809069fc41391c42bb.json"}, "display": {"href": "https://publications.scilifelab.se/publication/1f83014b47ad46809069fc41391c42bb"}}, "title": "Global gray-level thresholding based on object size.", "authors": [{"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal article", "published": "2016-04-00", "journal": {"volume": "89", "issn": "1552-4930", "issue": "4", "pages": "385-390", "title": "Cytometry A", "issn-l": "1552-4922"}, "abstract": "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. \u00a9 2016 International Society for Advancement of Cytometry.", "doi": "10.1002/cyto.a.22806", "pmid": "26800009", "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "cb.uu.se", "description": "Images", "key": "http://www.cb.uu.se/~petter/downloads/CellClusters/"}, {"db": "cb.uu.se", "description": "ImageJ and CellProfiler plugins", "key": "http://www.cb.uu.se/~petter/downloads/SIP/"}, {"db": "murphylab.web.cmu.edu", "description": "Images", "key": "http://murphylab.web.cmu.edu/data/2009_ISBI_Nuclei.html"}, {"db": "smal.we", "description": "Synthetic image data generator", "key": "http://smal.ws/wp/software/synthetic-data-generator/"}], "notes": [], "created": "2017-05-03T12:58:49.788Z", "modified": "2021-07-05T14:18:24.316Z"}, {"entity": "publication", "iuid": "47523d44b099420897fadd4070f0a53f", "links": {"self": {"href": "https://publications.scilifelab.se/publication/47523d44b099420897fadd4070f0a53f.json"}, "display": {"href": "https://publications.scilifelab.se/publication/47523d44b099420897fadd4070f0a53f"}}, "title": "PopulationProfiler: A Tool for Population Analysis and Visualization of Image-Based Cell Screening Data.", "authors": [{"family": "Matuszewski", "given": "Damian J", "initials": "DJ", "orcid": "0000-0002-6148-5174", "researcher": {"href": "https://publications.scilifelab.se/researcher/0329e24125c3443e99e51435bea6af85.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Puigvert", "given": "Jordi Carreras", "initials": "JC"}, {"family": "Sintorn", "given": "Ida-Maria", "initials": "IM", "orcid": "0000-0002-8307-7411", "researcher": {"href": "https://publications.scilifelab.se/researcher/8ff79494ec3842ffaa7448d2e3277f6b.json"}}], "type": "journal article", "published": "2016-03-17", "journal": {"volume": "11", "issn": "1932-6203", "issue": "3", "pages": "e0151554", "title": "PLoS ONE", "issn-l": "1932-6203"}, "abstract": "Image-based screening typically produces quantitative measurements of cell appearance. Large-scale screens involving tens of thousands of images, each containing hundreds of cells described by hundreds of measurements, result in overwhelming amounts of data. Reducing per-cell measurements to the averages across the image(s) for each treatment leads to loss of potentially valuable information on population variability. We present PopulationProfiler-a new software tool that reduces per-cell measurements to population statistics. The software imports measurements from a simple text file, visualizes population distributions in a compact and comprehensive way, and can create gates for subpopulation classes based on control samples. We validate the tool by showing how PopulationProfiler can be used to analyze the effect of drugs that disturb the cell cycle, and compare the results to those obtained with flow cytometry.", "doi": "10.1371/journal.pone.0151554", "pmid": "26987120", "labels": {"BioImage Informatics": "Service", "Bioinformatics (NBIS)": "Service"}, "xrefs": [{"db": "pmc", "key": "PMC4795740"}, {"db": "pii", "key": "PONE-D-15-54719"}], "notes": [], "created": "2018-10-28T08:19:20.514Z", "modified": "2023-06-19T13:00:20.163Z"}, {"entity": "publication", "iuid": "fd4b0b043ebe4f1287dceca89819d856", "links": {"self": {"href": "https://publications.scilifelab.se/publication/fd4b0b043ebe4f1287dceca89819d856.json"}, "display": {"href": "https://publications.scilifelab.se/publication/fd4b0b043ebe4f1287dceca89819d856"}}, "title": "Segmentation and Track-Analysis in Time-Lapse Imaging of Bacteria", "authors": [{"family": "Sadanandan", "given": "Sajith Kecheril", "initials": "SK"}, {"family": "Baltekin", "given": "Ozden", "initials": "O"}, {"family": "Magnusson", "given": "Klas E G", "initials": "KEG"}, {"family": "Boucharin", "given": "Alexis", "initials": "A"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "Jalden", "given": "Joakim", "initials": "J"}, {"family": "Elf", "given": "Johan", "initials": "J"}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal-article", "published": "2016-02-00", "journal": {"volume": "10", "issn": "1932-4553", "issue": "1", "pages": "174-184", "title": "IEEE J. Sel. Top. Signal Process.", "issn-l": null}, "abstract": null, "doi": "10.1109/jstsp.2015.2491304", "pmid": null, "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [], "notes": [], "created": "2017-05-05T13:01:48.956Z", "modified": "2021-07-05T14:18:24.451Z"}, {"entity": "publication", "iuid": "00a8c812874c4847b5151894e0bd5347", "links": {"self": {"href": "https://publications.scilifelab.se/publication/00a8c812874c4847b5151894e0bd5347.json"}, "display": {"href": "https://publications.scilifelab.se/publication/00a8c812874c4847b5151894e0bd5347"}}, "title": "Feature Augmented Deep Neural Networks for Segmentation of Cells", "authors": [{"family": "Sadanandan", "given": "Sajith Kecheril", "initials": "SK"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "book-chapter", "published": "2016-00-00", "journal": {"volume": null, "issn": "0302-9743", "issue": null, "pages": "231-243", "title": null, "issn-l": null}, "abstract": null, "doi": "10.1007/978-3-319-46604-0_17", "pmid": null, "labels": {"BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "bitbucket.org", "description": "Code", "key": "https://bitbucket.org/sajithks/fastcba/"}], "notes": [], "created": "2018-10-28T08:18:19.674Z", "modified": "2021-07-05T14:18:24.270Z"}, {"entity": "publication", "iuid": "0ec7d48c59e644a7b2afd4e3069656d7", "links": {"self": {"href": "https://publications.scilifelab.se/publication/0ec7d48c59e644a7b2afd4e3069656d7.json"}, "display": {"href": "https://publications.scilifelab.se/publication/0ec7d48c59e644a7b2afd4e3069656d7"}}, "title": "Bridging Histology and Bioinformatics\u2014Computational Analysis of Spatially Resolved Transcriptomics", "authors": [{"family": "Mignardi", "given": "Marco", "initials": "M"}, {"family": "Ishaq", "given": "Omer", "initials": "O"}, {"family": "Qian", "given": "Xiaoyan", "initials": "X"}, {"family": "Wahlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}], "type": "journal-article", "published": "2016-00-00", "journal": {"volume": null, "issn": "0018-9219", "issue": null, "pages": "1-12", "title": "Proc. IEEE", "issn-l": null}, "abstract": null, "doi": "10.1109/jproc.2016.2538562", "pmid": null, "labels": {"BioImage Informatics": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [], "notes": [], "created": "2017-11-01T08:24:47.401Z", "modified": "2021-07-05T14:18:24.293Z"}, {"entity": "publication", "iuid": "8a731b002afd4687b02d25ba783fe634", "links": {"self": {"href": "https://publications.scilifelab.se/publication/8a731b002afd4687b02d25ba783fe634.json"}, "display": {"href": "https://publications.scilifelab.se/publication/8a731b002afd4687b02d25ba783fe634"}}, "title": "Compaction of rolling circle amplification products increases signal integrity and signal-to-noise ratio.", "authors": [{"family": "Clausson", "given": "Carl-Magnus", "initials": "C"}, {"family": "Arng\u00e5rden", "given": "Linda", "initials": "L"}, {"family": "Ishaq", "given": "Omer", "initials": "O"}, {"family": "Klaesson", "given": "Axel", "initials": "A"}, {"family": "K\u00fchnemund", "given": "Malte", "initials": "M"}, {"family": "Grannas", "given": "Karin", "initials": "K"}, {"family": "Koos", "given": "Bj\u00f6rn", "initials": "B"}, {"family": "Qian", "given": "Xiaoyan", "initials": "X"}, {"family": "Ranefall", "given": "Petter", "initials": "P"}, {"family": "Krzywkowski", "given": "Tomasz", "initials": "T"}, {"family": "Brismar", "given": "Hjalmar", "initials": "H", "orcid": "0000-0003-0578-4003", "researcher": {"href": "https://publications.scilifelab.se/researcher/1ec23336e2ef4e298f340876f1136dce.json"}}, {"family": "Nilsson", "given": "Mats", "initials": "M", "orcid": "0000-0001-9985-0387", "researcher": {"href": "https://publications.scilifelab.se/researcher/197cf8ba83ba430f9712b2f4d94dc3e5.json"}}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "S\u00f6derberg", "given": "Ola", "initials": "O"}], "type": "comparative study", "published": "2015-07-23", "journal": {"volume": "5", "issn": "2045-2322", "issue": null, "pages": "12317", "title": "Sci Rep", "issn-l": "2045-2322"}, "abstract": "Rolling circle amplification (RCA) for generation of distinct fluorescent signals in situ relies upon the self-collapsing properties of single-stranded DNA in commonly used RCA-based methods. By introducing a cross-hybridizing DNA oligonucleotide during rolling circle amplification, we demonstrate that the fluorophore-labeled RCA products (RCPs) become smaller. The reduced size of RCPs increases the local concentration of fluorophores and as a result, the signal intensity increases together with the signal-to-noise ratio. Furthermore, we have found that RCPs sometimes tend to disintegrate and may be recorded as several RCPs, a trait that is prevented with our cross-hybridizing DNA oligonucleotide. These effects generated by compaction of RCPs improve accuracy of visual as well as automated in situ analysis for RCA based methods, such as proximity ligation assays (PLA) and padlock probes.", "doi": "10.1038/srep12317", "pmid": "26202090", "labels": {"BioImage Informatics": "Technology development", "Integrated Microscopy Technologies Stockholm": "Collaborative", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "srep12317"}, {"db": "pmc", "key": "PMC4511876"}], "notes": [], "created": "2017-05-02T12:56:40.263Z", "modified": "2021-07-07T13:54:46.097Z"}, {"entity": "publication", "iuid": "11fa4908f11749e2a8ed03dbef5a8055", "links": {"self": {"href": "https://publications.scilifelab.se/publication/11fa4908f11749e2a8ed03dbef5a8055.json"}, "display": {"href": "https://publications.scilifelab.se/publication/11fa4908f11749e2a8ed03dbef5a8055"}}, "title": "Automated classification of immunostaining patterns in breast tissue from the human protein atlas.", "authors": [{"family": "Swamidoss", "given": "Issac Niwas", "initials": "IN"}, {"family": "K\u00e5rsn\u00e4s", "given": "Andreas", "initials": "A"}, {"family": "Uhlmann", "given": "Virginie", "initials": "V"}, {"family": "Ponnusamy", "given": "Palanisamy", "initials": "P"}, {"family": "Kampf", "given": "Caroline", "initials": "C"}, {"family": "Simonsson", "given": "Martin", "initials": "M"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "Strand", "given": "Robin", "initials": "R"}], "type": "journal article", "published": "2013-03-30", "journal": {"volume": "4", "issn": "2229-5089", "issue": "Suppl", "pages": "S14", "title": "J Pathol Inform", "issn-l": "2229-5089"}, "abstract": "The Human Protein Atlas (HPA) is an effort to map the location of all human proteins (http://www.proteinatlas.org/). It contains a large number of histological images of sections from human tissue. Tissue micro arrays (TMA) are imaged by a slide scanning microscope, and each image represents a thin slice of a tissue core with a dark brown antibody specific stain and a blue counter stain. When generating antibodies for protein profiling of the human proteome, an important step in the quality control is to compare staining patterns of different antibodies directed towards the same protein. This comparison is an ultimate control that the antibody recognizes the right protein. In this paper, we propose and evaluate different approaches for classifying sub-cellular antibody staining patterns in breast tissue samples.\r\n\r\nThe proposed methods include the computation of various features including gray level co-occurrence matrix (GLCM) features, complex wavelet co-occurrence matrix (CWCM) features, and weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM)-inspired features. The extracted features are used into two different multivariate classifiers (support vector machine (SVM) and linear discriminant analysis (LDA) classifier). Before extracting features, we use color deconvolution to separate different tissue components, such as the brownly stained positive regions and the blue cellular regions, in the immuno-stained TMA images of breast tissue.\r\n\r\nWe present classification results based on combinations of feature measurements. The proposed complex wavelet features and the WND-CHARM features have accuracy similar to that of a human expert.\r\n\r\nBoth human experts and the proposed automated methods have difficulties discriminating between nuclear and cytoplasmic staining patterns. This is to a large extent due to mixed staining of nucleus and cytoplasm. Methods for quantification of staining patterns in histopathology have many applications, ranging from antibody quality control to tumor grading.", "doi": "10.4103/2153-3539.109881", "pmid": "23766936", "labels": {"Tissue Profiling": null, "BioImage Informatics": "Technology development", "Bioinformatics (NBIS)": "Technology development"}, "xrefs": [{"db": "pii", "key": "JPI-4-14"}, {"db": "pmc", "key": "PMC3678740"}], "notes": [], "created": "2017-05-04T14:56:00.153Z", "modified": "2021-07-05T14:18:24.301Z"}, {"entity": "publication", "iuid": "c715d94addb8457fb349e441761e4094", "links": {"self": {"href": "https://publications.scilifelab.se/publication/c715d94addb8457fb349e441761e4094.json"}, "display": {"href": "https://publications.scilifelab.se/publication/c715d94addb8457fb349e441761e4094"}}, "title": "Visualising individual sequence-specific protein-DNA interactions in situ.", "authors": [{"family": "Weibrecht", "given": "Irene", "initials": "I"}, {"family": "Gavrilovic", "given": "Milan", "initials": "M"}, {"family": "Lindbom", "given": "Lena", "initials": "L"}, {"family": "Landegren", "given": "Ulf", "initials": "U"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "S\u00f6derberg", "given": "Ola", "initials": "O"}], "type": "journal article", "published": "2012-06-15", "journal": {"volume": "29", "issn": "1876-4347", "issue": "5", "pages": "589-598", "title": "N Biotechnol", "issn-l": "1871-6784"}, "abstract": "Gene expression - a key feature for modulating cell fate-is regulated in part by histone modifications, which modulate accessibility of the chromatin to transcription factors. Until now, protein-DNA interactions (PDIs) have mostly been studied in bulk without retrieving spatial information from the sample or with poor sequence resolution. New tools are needed to reveal proteins interacting with specific DNA sequences in situ for further understanding of the orchestration of transcriptional control within the nucleus. We present herein an approach to visualise individual PDIs within cells, based on the in situ proximity ligation assay (PLA). This assay, previously used for the detection of protein-protein interactions in situ, was adapted for analysis of target PDIs, using padlock probes to identify unique DNA sequences in complex genomes. As a proof-of-principle we detected histone H3 interacting with a 26 bp consensus sequence of the Alu-repeat abundantly expressed in the human genome, but absent in mice. However, the mouse genome contains a highly similar sequence, providing a model system to analyse the selectivity of the developed methods. Although efficiency of detection currently is limiting, we conclude that in situ PLA can be used to achieve a highly selective analysis of PDIs in single cells.", "doi": "10.1016/j.nbt.2011.08.002", "pmid": "21906700", "labels": {"BioImage Informatics": "Collaborative", "PLA and Single Cell Proteomics": "", "Affinity Proteomics Uppsala": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "S1871-6784(11)00186-5"}], "notes": [], "created": "2017-05-04T14:55:23.757Z", "modified": "2023-04-14T13:56:31.053Z"}, {"entity": "publication", "iuid": "bb6fafd791694fa59add52f553a585f6", "links": {"self": {"href": "https://publications.scilifelab.se/publication/bb6fafd791694fa59add52f553a585f6.json"}, "display": {"href": "https://publications.scilifelab.se/publication/bb6fafd791694fa59add52f553a585f6"}}, "title": "Increasing the dynamic range of in situ PLA.", "authors": [{"family": "Clausson", "given": "Carl-Magnus", "initials": "C"}, {"family": "Allalou", "given": "Amin", "initials": "A"}, {"family": "Weibrecht", "given": "Irene", "initials": "I"}, {"family": "Mahmoudi", "given": "Salah", "initials": "S"}, {"family": "Farnebo", "given": "Marianne", "initials": "M"}, {"family": "Landegren", "given": "Ulf", "initials": "U"}, {"family": "W\u00e4hlby", "given": "Carolina", "initials": "C", "orcid": "0000-0002-4139-7003", "researcher": {"href": "https://publications.scilifelab.se/researcher/c50194fbc8524d95b7152663ccf17f29.json"}}, {"family": "S\u00f6derberg", "given": "Ola", "initials": "O"}], "type": "letter", "published": "2011-10-28", "journal": {"volume": "8", "issn": "1548-7105", "issue": "11", "pages": "892-893", "title": "Nat. Methods", "issn-l": "1548-7091"}, "abstract": null, "doi": "10.1038/nmeth.1743", "pmid": "22036742", "labels": {"BioImage Informatics": "Collaborative", "PLA and Single Cell Proteomics": "", "Affinity Proteomics Uppsala": "Collaborative", "Bioinformatics (NBIS)": "Collaborative"}, "xrefs": [{"db": "pii", "key": "nmeth.1743"}], "notes": [], "created": "2017-05-04T14:55:21.665Z", "modified": "2023-04-14T13:56:31.396Z"}]}