{"entity": "researcher", "timestamp": "2026-09-30T04:27:57.022Z", "family": "Windhager", "given": "Jonas", "initials": "J", "orcid": "0000-0002-2111-5291", "affiliations": ["Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland.", "Institute for Molecular Health Sciences, ETH Zurich, Zurich, Switzerland.", "Life Science Zurich Graduate School, ETH Zurich and University of Zurich, Zurich, Switzerland.", "SciLifeLab BioImage Informatics Facility and Department of Information Technology, Uppsala University, Uppsala, Sweden."], "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/researcher/24e51849979241a59823496569e1117a.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/researcher/24e51849979241a59823496569e1117a"}}, "publications": [{"entity": "publication", "iuid": "f60b781e25ef4d12ac42d587758da555", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f60b781e25ef4d12ac42d587758da555.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f60b781e25ef4d12ac42d587758da555"}}, "title": "Droplet microfluidics-based detection of rare antibiotic-resistant subpopulations in Escherichia coli from bloodstream infections.", "authors": [{"family": "Agnihotri", "given": "Sagar N", "initials": "SN", "orcid": "0000-0003-0943-6751", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f94bb0d6027246169cfca1126a80b6e8.json"}}, {"family": "Fatsis-Kavalopoulos", "given": "Nikos", "initials": "N", "orcid": "0000-0002-5081-0138", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/910a27f773b64bb6a1a15611b0524f34.json"}}, {"family": "Windhager", "given": "Jonas", "initials": "J", "orcid": "0000-0002-2111-5291", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/24e51849979241a59823496569e1117a.json"}}, {"family": "Tenje", "given": "Maria", "initials": "M", "orcid": "0000-0002-1264-1337", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/bd13273274574923b354ca9c605efc8f.json"}}, {"family": "Andersson", "given": "Dan I", "initials": "DI", "orcid": "0000-0001-6640-2174", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/f6dfd353332a4a5c906d28be61dece6f.json"}}], "type": "journal article", "published": "2025-07-04", "journal": {"title": "Sci Adv", "issn": "2375-2548", "volume": "11", "issue": "27", "pages": "eadv4558", "issn-l": "2375-2548"}, "abstract": "Population heterogeneity in bacterial phenotypes, such as antibiotic resistance, is increasingly recognized as a medical concern. Heteroresistance occurs when a predominantly susceptible bacterial population harbors a rare resistant subpopulation. During antibiotic exposure, these resistant bacteria can be selected and lead to treatment failure. Standard antibiotic susceptibility testing methods often fail to reliably detect these subpopulations due to their low frequency, highlighting the need for improved diagnostic approaches. Here, we present a droplet microfluidics method where bacteria are encapsulated in droplets containing growth medium and antibiotics. The growth of rare resistant cells is detected by observing droplet shrinkage under microscopy. We validated this method for three clinically important antibiotics in Escherichia coli isolates obtained from bloodstream infections and showed that it can detect resistant subpopulations as infrequent as 10-6 using only 200 to 300 droplets. In addition, we designed a multiplex microfluidic chip to increase the throughput of the assay.", "doi": "10.1126/sciadv.adv4558", "pmid": "40614180", "labels": [], "xrefs": [{"db": "pmc", "key": "PMC12227044"}], "notes": [], "created": "2026-09-23T09:02:41.246Z", "modified": "2026-09-23T09:02:41.411Z"}, {"entity": "publication", "iuid": "f3a79e7160374a9b88b0940a7c1e3740", "links": {"self": {"href": "https://publications-affiliated.scilifelab.se/publication/f3a79e7160374a9b88b0940a7c1e3740.json"}, "display": {"href": "https://publications-affiliated.scilifelab.se/publication/f3a79e7160374a9b88b0940a7c1e3740"}}, "title": "An end-to-end workflow for multiplexed image processing and analysis.", "authors": [{"family": "Windhager", "given": "Jonas", "initials": "J", "orcid": "0000-0002-2111-5291", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/24e51849979241a59823496569e1117a.json"}}, {"family": "Zanotelli", "given": "Vito Riccardo Tomaso", "initials": "VRT"}, {"family": "Schulz", "given": "Daniel", "initials": "D", "orcid": "0000-0002-0913-1678", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/569aaad70db640c5acc419e1618a9e38.json"}}, {"family": "Meyer", "given": "Lasse", "initials": "L", "orcid": "0000-0002-1660-1199", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/c3c872b5c77643a68d3cce1a7deff50f.json"}}, {"family": "Daniel", "given": "Michelle", "initials": "M"}, {"family": "Bodenmiller", "given": "Bernd", "initials": "B", "orcid": "0000-0002-6325-7861", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/a82f7b007b8349c398d5ee7ae3d5c564.json"}}, {"family": "Eling", "given": "Nils", "initials": "N", "orcid": "0000-0002-4711-1176", "researcher": {"href": "https://publications-affiliated.scilifelab.se/researcher/08cc33c9e84049a3883bb7cc52314225.json"}}], "type": "journal article", "published": "2023-11-00", "journal": {"title": "Nat Protoc", "issn": "1750-2799", "volume": "18", "issue": "11", "pages": "3565-3613", "issn-l": null}, "abstract": "Multiplexed imaging enables the simultaneous spatial profiling of dozens of biological molecules in tissues at single-cell resolution. Extracting biologically relevant information, such as the spatial distribution of cell phenotypes from multiplexed tissue imaging data, involves a number of computational tasks, including image segmentation, feature extraction and spatially resolved single-cell analysis. Here, we present an end-to-end workflow for multiplexed tissue image processing and analysis that integrates previously developed computational tools to enable these tasks in a user-friendly and customizable fashion. For data quality assessment, we highlight the utility of napari-imc for interactively inspecting raw imaging data and the cytomapper R/Bioconductor package for image visualization in R. Raw data preprocessing, image segmentation and feature extraction are performed using the steinbock toolkit. We showcase two alternative approaches for segmenting cells on the basis of supervised pixel classification and pretrained deep learning models. The extracted single-cell data are then read, processed and analyzed in R. The protocol describes the use of community-established data containers, facilitating the application of R/Bioconductor packages for dimensionality reduction, single-cell visualization and phenotyping. We provide instructions for performing spatially resolved single-cell analysis, including community analysis, cellular neighborhood detection and cell-cell interaction testing using the imcRtools R/Bioconductor package. The workflow has been previously applied to imaging mass cytometry data, but can be easily adapted to other highly multiplexed imaging technologies. This protocol can be implemented by researchers with basic bioinformatics training, and the analysis of the provided dataset can be completed within 5-6 h. An extended version is available at https://bodenmillergroup.github.io/IMCDataAnalysis/ .", "doi": "10.1038/s41596-023-00881-0", "pmid": "37816904", "labels": [], "xrefs": [{"db": "pii", "key": "10.1038/s41596-023-00881-0"}], "notes": [], "created": "2026-09-23T06:38:53.634Z", "modified": "2026-09-23T06:38:53.809Z"}]}