https://researchdata.se/en/catalogue Search results en Datasets This repository contains processed output data to reproduce figures and findings. Repository contains the following data: Spike-in normalised count data from single-cell Xpress-seq of aneuploid clonal cell lines, in long text R RDS format and SingleCellExperiment R RDS format. Segmented chromosome copy numbers and ploidies from DNA-seq of aneuploid clonal cell lines, in text format. Proteomics data from aneuploid clonal cell lines, in Excel and text format. Transcriptional burst kinetics inference of aneuploid clonal cell lines and fibroblasts from Reinius B et al., 2016 Nat Genet, in text format. Abstract Aneuploidy, resulting from whole-chromosome or segmental gains and losses, is generally detrimental to organismal fitness and is strongly associated with tumorigenesis. While sex chromosome aneuploidies are relatively well tolerated due to dedicated allelic dosage compensation mechanisms, the existence of comparable regulatory processes for autosomes remain debated. Here, we investigate transcriptional responses to autosomal aneuploidy and identify widespread dosage compensation across affected chromosomes. Using high-sensitivity, allele-resolved single-cell RNA sequencing of monoclonal cell expansions spanning diverse ploidies and degrees of aneuploidy, we uncover consistent transcriptional compensation of both chromosomal gains and losses. This response operates in a region-specific manner across both complete and segmental aneuploidies, revealing an unexpected flexibility in dosage regulation. We show that chromosome losses are compensated through increased transcriptional burst frequency from the remaining allele. Complementary proteomics analyses demonstrate additional buffering at the protein level, resulting in a substantial, though still incomplete, restoration of stoichiometric balance across autosomal aneuploid chromosomes for both copy number gains and losses. Analyses of cancer transcriptomes confirm that compensatory mechanisms are active in human tumours. Together, our findings establish dosage compensation as a genome-wide and dynamic response to gene dose imbalance. This mechanism extends beyond sex chromosomes, supports transcriptional homeostasis, and represents a fundamental and evolutionarily conserved mode of gene regulation active across species and in aneuploid cancer cells. Wed, 30 Sep 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-23055741 https://researchdata.se/en/catalogue/dataset/doi-10-5281-zenodo-23055741 Linköping University Papanicolaou, Natali Wettersten, Sebastian Maia, Guilherme Augusto Lentini, Antonio Reinius, Björn The plasma proteome from 85 individuals with ovarian tumors of benign (n = 23) or malignant (n=62) nature has been quantified using Illumina SomaSeq Discovery (SomaSeq), previously called Illumina Protein Prep (IPP), and Olink Explore HT (EHT). For 52 of these samples (9 benign and 43 malignant), there was also tumour tissue RNA data available. The data attached to this repository consists of processed analysis results from the comparative analysis between the SomaSeq and EHT datasets. Supplementary Data 1 includes protein-protein correlations based on SomaSeq. Supplementary Data 2 includes the DESeq2 results from transcriptomic analysis for transcripts encoding for proteins in the SomaSeq and EHT panels. There is also an accompanied README describing the columns of the data. Raw proteomic or transcriptomic data are not included. Tue, 25 Aug 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-32626056 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-32626056 Uppsala University Mikaela Moskov Stefan Enroth Emmy Borgmästars This dataset contains levels of circulating antibodies and proteins measured in dried blood spot (DBS) samples. Study participants from the general population in Stockholm and Gothenburg (Sweden) self-sampled blood using sampling kits (Capitainer AB) during the spring of 2021. - Levels of circulating antibodies were determined by multi-analyte serology using the Luminex platform. The data has been processed and normalised using a mixed model, followed by a Z-score transformation. - Levels of circulating proteins were determined by proximity extension assays using the Olink Explore Inflammation panel from the Olink platform and NULISA-Seq using the 250-plex inflammation panel from Alamar Biosciences. The raw data has been processed using ProtPQN (https://doi.org/10.5281/zenodo.12731705) , followed by a Z-score transformation. The files contain the following data and information: - antigen_info.csv: Information about the antigens used in the serology assays. - protein_data.csv: Proteomics measurements in wide format (each row a sample, each column a protein). - protein_info.csv: Information about the proteins included in the proteomics assays. - sample_info.csv: Information about the samples. - serology_data.csv: Serological measurements in wide format. - readme.txt: Additional details about the file contents. Access to this data can be granted for non-commercial validation purposes and upon reasonable request to the provided contact. A reasonable request should contain the following: - Name of PI and host organisation - Contact details - The scientific purpose of the data access request - Commitment to inform when the data has been used in a publication - Commitment not to host or share the data outside the requesting organisation - Statement of non-commercial use of data Thu, 08 Jan 2026 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28902911 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28902911 Royal Institute of Technology Leo Dahl Annika Bendes Maria Bueno Alvez Vincent Albrecht Hooman Aghelpasand Sofia Björkander Simon Kebede Merid Anja Mezger Max Käller Claudia Fredolini Åsa Torinsson Naluai Olof Beck Erik Melén Stefan Bauer Magnus Gisslén Niclas Roxhed Jochen Schwenk Data published here is the basis for the manuscript for Spectrochimica Acta Part A with the title: The structure of amyloid-beta(1-42) oligomers in membrane-mimetic environments. Here, we demonstrate that Aβ42 oligomers preserve their β-sheet structure in aqueous solution and in a membrane-mimicking environment consisting of either anionic or zwitterionic membranes. Structure and Aβ42 aggregation kinetics were hardly affected by the presence of lipids, showing only slight effects observed during the initial oligomer formation at low temperatures. Our isotope-edited infrared experiments reveal that the backbone carbonyl of V18 residue is located in β-sheets in the presence and in the absence of lipids. Such insensitivity of Aβ42 to the presence of lipid vesicles suggests a distinct aggregation behaviour of Aβ42, compared to Aβ40. The data is in .DPT format, which can be viewed using any text editing program. Tue, 02 Sep 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17045-sthlmuni-29910311 https://researchdata.se/en/catalogue/dataset/doi-10-17045-sthlmuni-29910311 Stockholm University Oleksandra Kurysheva Nina Mann Uliana Afonina Andreas Barth 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/en/catalogue/dataset/doi-10-17044-scilifelab-28711088 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28711088 Umeå University 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/en/catalogue/dataset/doi-10-17044-scilifelab-27368322 https://researchdata.se/en/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 ANCA-associated vasculitides (AAV) are rare but severe disorders. Little is known about the pathophysiology of AAV. In this study, we analyzed the relative levels of 181 proteins using proximity extension assay (Olink) in plasma samples from patients with active AAV (n=66), matched population controls (n=138) and disease controls with rheumatoid arthritis (n=31) and systemic lupus erythematosus n=14), respectively. Distinct sets of upregulated proteins were identified in subgroups of patients with AAV defined by ANCA (PR3-ANCA and MPO-ANCA, respectively) compared with controls. Fri, 13 Jun 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-29291642 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-29291642 Region Uppsala Johanna Dahlqvist Erik Hellbacher The plasma proteome was analyzed using the proximity extension assay (PEA) as implemented in the Explore HT-version (Olink Proteomics AB, Uppsala, Sweden). The samples were analyzed at the Olink Service Laboratory in Uppsala, Sweden. In brief, the PEA is based on pairs of antibodies equipped with probes, DNA single-strand oligonucleotide reporter molecules, that bind to their respective target if present. Target binding by both probes in close proximity generates double-stranded DNA amplicons which are then quantified by next generation sequencing. Here, 5416 unique proteins were characterized in each of the 404 samples. The samples were randomized across plates with respect to cohort and diagnose (benign or malignant). In the resulting data-file provided by the analysis platform, each individual protein measurement, assay and sample is labelled depending on passing or failing quality control as provided by the instrument software. Here, a total on 768 individual measurements (corresponding to 0.036%, 768/(404*5416)) did not pass quality control and were removed from further analyses. Two proteins (apolipoprotein E (APOE) and fibronektin 1 (FN1)) had detection rates below 95% across all samples and were removed from further analyses. The resulting NPX values are on a log2 scale and in the logarithmic phase of the curve, one (1) increase of the NPX value corresponds to a doubling of the protein content. In the resulting data, a high NPX value corresponds to a high protein concentration. Each of the measured proteins has a lower limit of detection (LOD) given in the same NPX-scale which is determined at run time. Here, measurements under LOD were kept as is in the downstream analysis. After quality control, the detection rate across all samples were 99.5 to 99.8%. Fri, 04 Apr 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-27233412 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-27233412 Uppsala University Mikaela Moskov Julia Hedlund Lindberg Maria Lycke Emma Ivansson Ulf Gyllensten Karin Sundfeldt Karin Stålberg Stefan Enroth This dataset and metadata record describes the plasma proteome profiles used to create a detailed map of protein levels in human blood across major diseases. The open-access version of this dataset, protein levels per disease, is available on the Human Protein Atlas (www.proteinatlas.org (http://www.proteinatlas.org) ). This dataset includes plasma profiles of disease-specific groups and longitudinal analyses of healthy individuals. All plasma samples are EDTA plasma, collected following the ethical principles of the Declaration of Helsinki with informed consent. Comprehensive blood protein profiling across diseases and healthy individuals was conducted using the Proximity Extension Assay. The Olink Explore 1.5 and Olink Explore HT platforms were utilized for this analysis. The study includes four distinct datasets:- Pan-Disease: Plasma samples from 6,121 individuals across 59 diseases. (Olink Explore 1536) - Wellness: 100 individuals followed longitudinally over two years. (Olink HT) - BAMSE: 100 individuals followed from childhood to early adulthood. (Olink HT) - UCAN: Approximately 2,000 plasma samples from breast, ovarian, prostate, colorectal, and lung cancer patients. (Olink HT) Pan-Disease Cohorts:- Plasma samples from 6,121 individuals across 59 diseases, classified into cardiovascular, metabolic, cancer, psychiatric, autoimmune, infectious, and pediatric diseases. - Plasma samples from approximately 2,000 patients diagnosed with breast, ovarian, prostate, colorectal, and lung cancer patients from the U-CAN biobank (Uppsala and Umeå Universities, Uppsala Biobank, Biobanken Norr). Longitudinal Cohorts of Healthy Individuals:- 100 individuals (aged 50-65) followed at six visits over two years (Swedish SciLifeLab SCAPIS Wellness Profiling - S3WP program, recruited from SCAPIS). - 100 individuals followed at four-time points from childhood to early adulthood (Swedish population-based birth cohort BAMSE). Access and Data Availability (Important information)This entry is a metadata-only record describing the Human Disease Blood Atlas plasma proteomics datasets (Olink Explore 1.5 / Olink Explore HT, Proximity Extension Assay). Individual-level raw NPX values and individual-level clinical metadata cannot be shared due to ethical, consent, and governance constraints across the contributing cohorts. The open-access version of the resource is available via the Human Protein Atlas and provides group-level/aggregated protein abundance summaries with limited clinical metadata to prevent identification of individuals. What is available publicly - Aggregated / group-level protein abundance per disease (and other pre-defined groupings) through the Human Protein Atlas. What is not available - Individual-level raw NPX (or any row-level per-sample matrix) - Individual-level clinical metadata, coded IDs, or linkable subject-level information Requests for access Requests for individual-level NPX data cannot be granted through this record. Researchers are instead encouraged to: - Use the publicly available aggregated resource for hypothesis generation; and/or - Pursue a collaboration with the relevant cohort custodian for a clearly defined analysis where outputs can be shared only at an aggregated level, in line with cohort governance and participant consent. Such requests must be directed to the appropriate cohort owner (listed as a co-author on the associated Science publication, https://www.science.org/doi/10.1126/science.adx2678). Fri, 04 Apr 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28577390 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28577390 Royal Institute of Technology Fredrik Edfors Arfwidsson Mathias Uhlen Maria Bueno Alvez The dataset consists of a fasta file including 36 bacterial species and the human proteome obtained from the Uniprot database. The 36 bacterial species were chosen based on ≥ 1 % detection of total reads in 16S sequencing data of dried cervicovaginal fluid from paper cards. This dataset can be used as a reference file for quantification of bacterial proteins using maxquant. The fasta file contains a representative proteome for the following species: Proteome_ID Species UP000234775 Aerococcus christensenii UP000016519 Alloscardovia omnicolens UP000005984 Anaerococcus lactolyticus UP000003191 Bifidobacterium breve UP000003503 Dialister micraerophilus UP000005947 Fannyhessea vaginae UP000005392 Fusobacterium animalis UP000002521 Fusobacterium nucleatum UP000258888 Gardnerella vaginalis UP000255264 Haemophilus pittmaniae UP000004115 Lactobacillus iners UP000003242 Megasphaera lornae UP000002631 Mycoplasma hominis UP000004206 Peptostreptococcus anaerobius UP000002786 Prevotella bivia UP000033103 Sneathia vaginalis UP000000423 Ureaplasma parvum UP000029628 Veillonella montpellierensis UP000003744 Anaerococcus tetradius UP000253807 Haemophilus parainfluenzae UP000067598 Lactobacillus crispatus UP000189488 Lactobacillus gasseri UP000327236 Lactobacillus jensenii UP000318911 Lactobacillus mulieris UP000070160 Megasphaera hutchinsoni UP000070531 Prevotella amnii UP000254072 Prevotella disiens UP001254699 Sneathia sanguinegens UP000238573 Streptococcus anginosus UP000008693 Bifidobacterium dentium UP000005388 Streptococcus urinalis UP000189488 Lactobacillus gasseri UP000000821 Streptococcus agalactiae UP000000625 Escherichia coli UP000008816 Staphylococcus aureus UP000000431 Chlamydia trachomatis UP000005640 Homo sapiens Wed, 26 Mar 2025 00:00:00 GMT https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28359677 https://researchdata.se/en/catalogue/dataset/doi-10-17044-scilifelab-28359677 Uppsala University Emmy Borgmästars Stefan Enroth Julia Hedlund Lindberg Karin Sundfeldt Ulf Gyllensten