https://researchdata.se/sv/catalogue Search results sv Sample Collections from Human SubjectsCardiac ischemic biopsies were obtained from 30 patients undergoing coronary artery bypass surgery at the Sahlgrenska University Hospital, Gothenburg, Sweden, due to significant atherosclerotic blockage in the epicardial coronary arteries. The surgery and sample collections were performed between 7 February 2013 and 1 February 2017. All patients were examined through echocardiography prior to operation, and the ejection fraction (EF) was measured. Non-ischemic biopsies from the left ventricle were obtained from 14 subjects undergoing aortic valve replacement in the same hospital, with angiography verified absence of coronary artery disease in any of the major myocardial coronary artery branches. Individual level data on age, sex, and other information is available in Data S1. All biopsies were collected from the left ventricle septum region with a 1 mm needle. The average weight of collected tissues samples was 100 mg and these were stored at -80°C until analysis. All patients gave informed and written consent. Patient characteristics (including diabetes status) were either collected as the patients entered the study or retrieved later from their medical charts. The diagnoses were based on ICD-codes from patient records and national databases. The study was approved by the Gothenburg Regional Ethics Committee and done according to the Declaration of Helsinki (Dnr 064-14). RNA extraction and sequencingTotal RNA was isolated from biopsies relating to 44 samples from the left ventricle using RNeasy Fibrous Tissue Mini kit (QIAGEN) and 24 samples from epicardial fat using RNeasy Lipid Tissue Mini kit (QIAGEN). These 68 samples were processed with SMARTer® Stranded RNA-Seq Kit (Takara Bio) for reverse transcription, generation of double stranded cDNA and subsequent library preparation. All libraries were quantified with the Fragment Analyzer using the standard sensitivity NGS kit (Agilent Technologies), pooled in equimolar concentrations and quantified with a Qubit Fluorometer (ThermoFisher Scientific), the library pool was further diluted and sequenced using 150 cycles on an Illumina NextSeq500. Data and Code Availability• The processed transcriptomics data (raw counts and TPM), clinical, and demography data can be retrieved from Data S1. • The code for the analysis and visualization (grouped based on the figure numbers) is available at https://github.com/muharif/2025.Arif_Doran_etal_IschemicHeartDisease. • The data presented in this paper contain sensitive information that cannot be shared openly. Work on submitting the data to FEGA Sweden has been initiated. FEGA Sweden is a national node of the Federated European Genome-phenome Archive (FEGA), which allows data to be shared under controlled access. The datasets in FEGA Sweden are findable through the European Genome-phenome Archive web portal (https://ega-archive.org). Wed, 20 May 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-32270418 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-32270418 Göteborgs universitet Muhammad Arif Transcription factors (TFs) efficiently locate their target DNA sequences by combining three-dimensional diffusion and one-dimensional sliding on nonspecific DNA. To balance rapid sliding with strong specific binding, TFs were proposed to switch between search and recognition conformations. For E. coli lac repressor (LacI), the folding of the hinge helices has been implicated in the conformational switch. Here, we tested how mutations in the hinge region impact the search speed and binding stability. Based on molecular dynamics simulations, we selected two LacI mutants favoring either search or recognition conformation. We measured the binding kinetics of the mutants both in vitro on DNA microarrays with 2,479 different Lac operators and in vivo via single-molecule experiments. We identified a mutation that enhances the specificity but reduces binding strength globally, and another mutation that makes the operator binding stronger but also reduces the specificity. However, the altered specificity impacts the search time less than expected. Instead, the major effect was impaired dissociation in response to IPTG induction for the strongly binding mutant. Together with earlier reports of affinity–inducibility trade-offs in LacI, our data support the model in which the trade-off is between binding stability and inducibility rather than between speed and binding stability. This repository contains the molecular dynamics (MD) simulation datasets, analysis scripts, and code necessary to reproduce the results of our study. It also provides information on the datasets from single-molecule live-cell experiments and Protein Binding Microarray (PBM) assays, which are available in the BioImage Archive. File list: - Analysis: Analysis.tar - For plotting all figures in the main text and supplementary - Code: Code.tar - Utility scripts - Data: - Folders with prefix: 'Microscopy_Data_' - DataSets for Protein Binding Microarray experiments and Single Molecule experiments (raw fluorescence images), download these data from the link provided in 'Related material'. - Folders with prefix: 'MD_Data' - Data set for molecular dynamics simulations. README.txt: Instructions for setting up folder's structure and coding environment for running this project. Tue, 31 Mar 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-29040599 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-29040599 Uppsala universitet Jinwen Yuan Malin Lüking Spartak Zikrin Beer Chakra Sen Emil Marklund David van der Spoel David Fange Johan Elf Understanding and manipulating the spread of mobile genetic elements (MGEs) represents a great challenge with potential benefits across synthetic biology, agriculture and medicine. In this study, we use a time-lapse, imaging-based approach to characterize conjugative plasmid dynamics at the molecular, single-cell, and population levels. This Dataset contains code used for image analysis, bioinformatic analysis, agent-based modelling, and figure generation for "Molecular kinetics dictate population dynamics in CRISPR-based plasmid defense", as well as intermediate single-cell data processed from raw images. See link to the publication in Related Material. Raw image data is included in the linked BioImage archive repository. Specifically, the image analysis code contained here performs segmentation, fluorescent dot detection, and cell classification to track conjugative plasmid population dynamics in microfluidic growth chambers. The bioinformatic analysis code performs a comparative analysis of CRISPR spacer and toxin-antitoxin system presence across a broad range of bacterial plasmids. Finally, the agent-based simulation code facilitates simulations of plasmid population dynamics in bacterial populations. Using this code, we can simulate plasmid population dynamics using single-cell biophysical parameters as inputs, and directly compare with experimental imaging-based data. Mon, 23 Mar 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-31017391 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-31017391 Uppsala universitet Daniel Jones Luke Richards Danna Lee Jakub Wiktor Axel Truedson Johanna Cederblad Clear cell renal cell carcinoma (ccRCC) is an aggressive kidney cancer subtype frequently associated with poor prognosis. Most ccRCC cases are asymptomatic in early stages and symptomatic mostly in advanced stages. Furthermore, the heterogeneity of ccRCC presents a challenge to design new treatments. In this study, using proximity extension assay (PEA), we analyzed blood samples from 134 patients with ccRCC and from 111 age- and gender-matched healthy donors. We identified a panel of seven proteins (ANXA1, ESM1, FGFBP1, MDK, METAP2, SDC1, and TFPI2) that are associated with clinicopathological parameters and patient survival. These biomarkers can differentiate patients with ccRCC from the control individuals with high diagnostic sensitivity and specificity. Moreover, by studying protein expression in solid tumors from the same ccRCC patients, we revealed associations between the panel biomarkers and proteins in the TGF-β and VHL-HIF signaling pathways. We found that most tumor promoting biomarkers were positively associated with TGF-β signaling and HIF-2α, and negatively associated with pVHL and HIF-1α. We also found that most tumor suppressing biomarkers were positively associated with pVHL and HIF-1α and negatively associated with TGF-β signaling and HIF-2α. For ccRCC patients, the blood protein biomarkers that were connected to poor prognosis and TGF-β/HIF-2α signaling, as identified in this study, are potentially important assets in personalized medicine. We used an Olink panel to measure protein levels in clear cell renal cell carcinoma (ccRCC) patients (N=134) and healthy controls (N=111). 92 oncology-related protein levels are measured across all samples (Supplementary Data 1), and the dataset is corrected for patient age (Supplementary Data 2). 80 proteins are significantly altered in ccRCC patients compared to controls (Supplementary Data 3). Using the top 50 most significantly altered proteins, we trained a random forest (RF) model, with cross-validation (Supplementary Data 4 and 5). The top seven significantly altered proteins are sufficient to perfectly (AUC=1) classify patients and healthy controls (Supplementary Data 6). We further trained an elastic-net penalized logistic regression (ENLR) model using the top seven proteins, which also resulted in a perfect classifier. Use of random sets of seven proteins and their combinations are not as significant (Supplementary Data 7-10). We explored the correlations between transforming growth factor-β (TGF-β), VHL, and hypoxia signaling pathway protein expressions (TGFBR1-Full length receptor (FL), TGFBR1-intracellular domain (ICD), HIF-1A, HIF-2A, pVHL, pSMAD2/3) in solid tumors and the plasma protein levels from the same cohort (Supplementary Data 11-12). The names and accession numbers (UniProt) for the Olink proteins are listed in Supplementary Data 13. Levels of the TGFB and VHL pathway proteins in solid tumors are given in Supplementary Data 14 and the antibodies used in immunoblotting (IB) are listed in Supplementary Data 15. Lists of protein names measured in solid tumor samples vs protein names measured in plasma are in Supplementary Data 16. Mon, 18 Aug 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28711088 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28711088 Umeå universitet Cemal Erdem Pramod Mallikarjuna Ruben Beorlegui Anders Larsson Börje Ljungberg Masood Kamali-Moghaddam Maréne Landström The work has been published as Parajuli et al. (2025) Diabetologia (https://doi.org/10.1007/s00125-025-06502-7) Frequent self-sampling of blood has the potential to identify early, disease-predictive markers, including proteins. In a study to test this hypothesis, we conducted regular microsampling of a mouse model over 14 days and monitored their molecular response to a type 1 diabetes (T1D)-associated virus. This longitudinal approach involved the collection of dried blood samples, which were subsequently analysed for 92 circulating proteins. The data revealed transient molecular changes in the virus-infected mice. Utilising machine learning techniques, we achieved a prediction accuracy of over 90% for infection status after day 2 post-infection. This high level of accuracy enabled timely treatment interventions with immune serum, which could potentially prevent the onset of diabetes in the infected animals. The data of this study underscores the utility of frequent blood microsampling as a method for monitoring disease progression during the pre-symptomatic phase, allowing for prompt medical interventions of immune-mediated inflammatory diseases, including T1D. Description of data files:- Mouse DBS_Batch 1_ProtPQN data_rmd1021.csv: ProtPQN normalised NPX values for Study batch 1. The signals are in log2-scale. All columns not described below contain protein measurements. - Mouse DBS_Batch 2_ProtPQN data.csv: ProtPQN normalised NPX values for Study batch 2. The signals are in log2-scale. All columns not described below contain protein measurements. - zscore_data_combined.csv: Z-score transformed measurements for both studies. All columns not described below contain protein measurements. - sample information study batch 1 and 2.csv: Sample information for all of the mice. - Mouse DBS_NPX_below LOD.xlsx; Raw data from Olink Signature NPX software for Study batch 1. Values are reported as NPX values and are in log2-scale. This file contains values for limit of detection (LOD) per protein. - Mouse DBS Plate 2_NPX_belowLOD.xlsx: Raw data from Olink Signature NPX software for Study batch 2. Values are reported as NPX values and are in log2-scale. This file contains values for limit of detection (LOD) per protein Thu, 31 Jul 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27368322 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27368322 Karolinska Institutet Anirudra Parajuli Annika Bendes Fabian Byvald Virginia M. Stone Emma Ringqvist Marta Butrym Emmanouil Angelis Sophie Kipper Stefan Bauer Niclas Roxhed Jochen Schwenk Malin Flodström Tullberg This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper "Anti-correlation of LacI association and dissociation rates observed in living cells" (see Related Material). The raw data consists of microscopy images and qPCR files. The analysis and code consist of data analysis, output from the analysis, its post-processing, simulations, and which figure the data was used in. Experimental data, and analysis scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis, code & figure generation to combine for specific figures. For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition and which figure the data was used in. All experiments were performed at 30 degrees Celsius. Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt. Thu, 02 Jan 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-26425576 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-26425576 Uppsala universitet Vinodh Kandavalli Spartak Zikrin Johan Elf Daniel Jones This repository contains all raw data, software tools for data analysis, and scripts for generating the figures and tables provided in the main text and supplementary information of the manuscript "Dynamic binding of the bacterial chaperone Trigger factor to translating ribosomes in Escherichia coli". The bacterial chaperone Trigger factor (TF) binds to ribosome-nascent chain complexes (RNCs) and co-translationally aids the folding of proteins in bacteria. Here, we used single-particle tracking (SPT) to measure TF binding to translating ribosomes inside living Escherichia coli. TF displays distinct binding modes — longer (ca 1 s) and shorter (ca 50 ms) RNC bindings. We conclude that TF, on average, stays bound to the RNC for only a fraction of the translation cycle. Further, binding events are interrupted only by transient excursions to a freely diffusing state (ca 40 ms), suggesting a highly dynamic binding and unbinding cycle of TF in vivo. We also show that TF competes with SRP for RNC binding, and in doing so, tunes the binding selectivity of SRP. The data and corresponding analyses have been divided into folders corresponding to different experimental conditions. Each experimental condition folder contains raw microscopy images ("RawData") with three phase contrast images ("A_Phase"), one brightfield image ("B_Bf"), and a stack of fluorescence images ("C_553", "C_546", or "C_638", depending on what laser was used). The "Tracking_EXP_YY_YYYY" folder contains single-particle tracking analysis of the corresponding raw data, and the "HMM_analysis" folder contains HMM-based diffusion analysis of the single-particle trajectories. The "Tools" folder contains all software for data analysis, and "Figures_and_tables_scripts" contains scripts for generating figures and tables. Fri, 29 Nov 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27637464 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-27637464 Uppsala universitet Tora Hävermark Mikhail Metelev Erik Lundin Ivan L Volkov Magnus Johansson The intracellular position of genes may impact their expression, but it has not been possible to accurately measure the 3D position of chromosomal loci. In 2D, loci can be tracked using arrays of DNA-binding sites for transcription factors (TFs) fused with fluorescent proteins. However, the same 2D data can result from different 3D trajectories. Here, we have developed a deep learning method for super-resolved astigmatism-based 3D localization of chromosomal loci in live E. coli cells which enables a precision better than 61 nm at a signal-to-background ratio of ~4 on a heterogeneous cell background. Determining the spatial localization of chromosomal loci, we find that some loci are at the periphery of the nucleoid for large parts of the cell cycle. Analyses of individual trajectories reveal that these loci are subdiffusive both longitudinally (x) and radially (r), but that individual loci explore the full radial width on a minute time scale. This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing. Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables. For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius. Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt. Thu, 31 Oct 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-25998934 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-25998934 Uppsala universitet Praneeth Karempudi Konrad Gras Elias Amselem Spartak Zikrin Dvir Schirman Johan Elf In Escherichia coli, it is debated whether the two replisomes move independently along the two chromosome arms during replication or if they remain spatially confined. Here, we use high-throughput fluorescence microscopy to simultaneously determine the location and short-time-scale (1 s) movement of the replisome and a chromosomal locus in the same cell throughout its cell cycle. The assay is performed for several loci. We find that (i) the two replisomes are confined to a region of ~250 nm and ~120 nm along the cell’s long and short axis, respectively, (ii) the chromosomal loci move to and through this region sequentially based on their distance from the origin of replication, and (iii) when a locus is being replicated, its short time-scale movement slows down. This behavior is the same at different growth rates. In conclusion, our data supports a model with DNA moving towards spatially confined replisomes at replication. This dataset contains the raw data, analysis and code needed to generate the figures presented in the paper. The raw data consists of microscopy images. The analysis and code consists of analysis code, output from the analysis and its post-processing. Experimental data, analysis and plot scripts are all organized using unique IDs (UID; seen for example in the filenames of experimental data below). The README.txt file in the Analysis_of_data folder describes which UIDs for data, analysis & figure generation to combine for specific figures/tables. For the microscopy experiments (has the prefix Microscopy_Data_ in their filenames) there is information on the growth condition, strain genotypes, which positions corresponds to which genotype, and also which figure the data was used in. All experiments were performed at 30 degrees Celsius. Details on the output from microscopy experiments can be found in the file MicroscopyInformation.txt. Thu, 20 Jun 2024 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-25907971 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-25907971 Uppsala universitet Konrad Gras David Fange Johan Elf This dataset contains the raw images as well as the analysis pipelines and scripts used in the paper "Multi-site assessment of reproducibility in high-content live cell imaging data".  The Original data-2D.rar file contains the raw timelapse images of HT1080 cell line stably expressing H2B-EGFP and Lifeact-mCherry seeded on collagen I coated glass surface. Migration behavior of the cells was recorded in 5 min intervals for 6 h with fluorescent light microscopes equipped with environmental chamber. The experiment was performed by 3 labs, 3 person in each lab, 3 independent experiments by each person, 3 technical replicates in each experiment, and two conditions (control and ROCK inhibition) for each technical repliates.  The Data processing and analysis-2D.rar file contains the Matlab, CellProfiler, ImageJ, and R pipelines and scripts used in this study to process, quantify, and analyze the images. Detailed procedure could be found in the "Image processing and analysis procedures.txt" file within this .rar file. The 3D Image data from Lab 1.zip and 3D Image data from Lab 2.zip contain the raw images and the quantified results of the 3D migration assay from Lab 1 and Lab 2, respectively. The experiment was performed with HT1080 cell line stably expressing H2B-EGFP and Lifeact-mCherry embedded in 2.5mg/ml or 6mg/ml collagen I gels. The invasion of the cells from 3D spheroid was recorded with confocal microscopy 24 h after seeding. The experiment was performed by 2 labs, 3 independent experiments in each lab, 3 technical replicates in each experiment, and two conditions (2.5 mg/ml and 6 mg/ml of collagen I) for each technical repliates.  The Meta data of the 3D experiment.zip contains the meta data of the 3D image data from Lab 1 (Radboudumc) and Lab 2 (Crick) as well as the software to read the meta data. After unzipping, ISAcreator program should be used to read the ISAfiles of Lab 1 or Lab 2. The Fiji Plugins and parameters for 3D image data analysis.rar contains the Fiji plugins and also the parameters used during the 3D image data analysis. The 3D Data Analysis Scripts.rar contains the R scripts used in this study to analyze the 3D data set, as well as the quantified results needed by the R scripts. The Supplementary Materials 2-8.rar contains 2D experimental protocol (supplementary materials 2-4), 2D experimental survey (supplementary materials 3), and 3D experimental and image analysis protocols (supplementary materials 5-8) that are used in this study. We encourage reuse using the same CC BY 4.0 License. Thu, 01 Dec 2022 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-21407402 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-21407402 Karolinska Institutet Jianjiang Hu Xavier Serra-Picamal Gert-Jan Bakker Marleen Van Troys Sabina Winograd-katz Nil Ege Xiaowei Gong Yuliia Didan Inna Grosheva Omer Polansky Karima Bakkali Evelien Van Hamme Merijn van Erp Manon Vullings Felix Weiss Jarama Clucas Anna Dowbaj Erik Sahai Christophe Ampe Benjamin Geiger Peter Friedl Matteo Bottai Staffan Strömblad