https://researchdata.se/en/catalogue Search results en It contains data sets from the publication Andronico et al, 2026 (DOI: TBD). with the details for citation provided in the README file. Please cite this item as: Luca A. Andronico, Cenk O. Gurdap, Abishek Arora, Franziska Ragaller, Patrick A. Sandoz, Yidan Jiang, Sarantis Giatrellis, Leonard L. de Boer, Valentina Carannante, Sofiia Iskrak, Jaromir Mikes, Marcus Buggert, Anders Österborg, Björn Önfelt, Andrey Klymchenko, Petter Brodin, Erdinc Sezgin DOI: 10.17044/scilifelab.31882975 It contains microscopy images and excel sheets of the data. Abstract Cell membranes undergo biophysical remodelling as an adaptation to the surroundings and to perform specific biological functions. However, the extent and relevance of such changes in human immune cells remain unknown, largely due to the lack of single-cell and multidimensional methodologies. Here, we apply a cytometry-based method to fill this gap by combining biophysical profiling with simultaneous analysis of immune cell markers. This platform reveals notable cell type-dependent plasma membrane order heterogeneity in immune cells. By sorting immune cells according to their membrane order and performing transcriptome and spatial surface proteome analyses together with functional tests, we show that plasma membrane order can be used to identify subsets of immune cells with distinct phenotypes and functional behaviours. Our findings demonstrate a broad heterogeneity of plasma membrane order in immune cells that will provide a more precise definition of immune cell states based on their biophysical properties in health and disease. Data usage Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License. Data Content Excel files for graphs lsm files for raw microscopy images Software to open files .csv - Microsoft excel .lsm - Fiji (https://imagej.net/Fiji.html#Downloads) Mon, 03 Aug 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-31882975 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-31882975 Karolinska Institutet Erdinc Sezgin Abishek Arora Franziska Ragaller Patrick A. Sandoz Yidan Jiang Sarantis Giatrellis Valentina Carannante Sofiia Iskrak Jaromir Mikes Marcus Buggert Anders Österborg Björn Önfelt Andrey S. Klymchenko Petter Brodin Luca Andronico Leonard De Boer Cenk Gurdap It contains data sets from the publication, with the details for citation provided in the README file. Please cite this item as: Cenk O. Gurdap, Dunya Aydos, Luca A. Andronico, Gábor Tóth, Tugce Ceker, John Cowgill, Irem Muge Akbulut Koyuncu, Neslihan Basak, Jaromir Mikes, Andrey S. Klymchenko, Federico Pietrocola, Petter Brodin, Ingela Lanekoff, Verda Bitirim, Erdinc Sezgin DOI: 10.17044/scilifelab.30406327 It contains raw microscopy images, figures, Prism files, and excel sheets of the data. Abstract Biophysical properties of cells determine cellular physiology. Leveraging these properties for biomedical applications demands the ability to measure multiple parameters simultaneously across millions of cells and diverse cell types. However, current technologies are limited by throughput and low dimensionality. Here, we introduce spectral biophysical cytometry (SBC), a high-throughput platform that integrates environment-sensitive nanosensors with spectral flow cytometry to resolve multi-parametric biophysical properties of immune cells at single-cell resolution. By employing fluorescent nanosensors that report membrane order, mitochondrial potential, and membrane potential, SBC enables simultaneous quantification of key cellular physical states across diverse immune cell populations. When applied to peripheral blood mononuclear cells, SBC reveals cell-type–specific biophysical heterogeneity and identifies distinct remodeling signatures associated with atherosclerosis. In particular, T cell subsets exhibit significant alterations in membrane order and mitochondrial depolarization, reflecting coordinated changes in lipid composition and metabolic pathways. Integration with lipidomics and transcriptomics demonstrates that nanosensors enable detection of biophysical shifts that correlate with dysregulated lipid metabolism and mitochondrial function, providing mechanistic insight into immune dysfunction in disease. Importantly, SBC achieves rapid, label-efficient profiling using commercially available instrumentation, enabling scalable biomarker discovery directly from blood samples and establishing a powerful strategy for linking biophysical phenotypes to immune cell function. Data usage Researchers are welcome to use the data contained in the dataset for any projects. Please cite this item upon use or when published. We encourage reuse using the same CC BY 4.0 License. Data Content Excel files and Prism files for graphs fastq.gz files for omics Software to open files .xlsx or .cvs - Microsoft excel .pzfx or .prism – GraphPad Prism (https://www.graphpad.com/features) Mon, 22 Jun 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-30406327 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-30406327 Karolinska Institutet Erdinc Sezgin Dunya Aydos Luca Andronico Gabor Toth Tugce Ceker john cowgill Irem Muge Akbulut Koyuncu Neslihan Basak Jaromir Mikes Andrey S. Klymchenko Federico Pietrocola Petter Brodin Ingela Lanekoff Verda Bitirim Cenk Gürdap