https://researchdata.se/sv/catalogue Search results sv BackgroundEndoplasmic reticulum (ER) stress and its adaptive signaling through the unfolded protein response (UPR) are increasingly implicated in driving tumor progression and reshaping the tumor microenvironment in hepatocellular carcinoma (HCC). Among the UPR sensors, PERK (EIF2AK3) is thought to play a central role in enabling tumor cell survival under stress and promoting stromal activation, fibrosis, and inflammatory signaling. Additionally, ER stress–regulated secreted factors such as GP73 (GOLM1) and extracellular GRP78 may mediate communication between malignant cells and surrounding stromal populations, contributing to a pro-tumorigenic microenvironment.ObjectiveThe overall objective of both projects is to investigate how modulation of ER stress and UPR signaling influences HCC development, tumor progression, and tumor–stroma interactions. Specifically, the first project aims to determine whether pharmacological inhibition of PERK using a selective small-molecule inhibitor (AMG-PERK 44) can suppress ER stress-driven tumor growth and stromal activation, and to define the molecular mechanisms underlying ER stress-dependent communication between malignant cells and hepatic stellate cells, with a focus on GP73- and GRP78-mediated signaling. The second project seeks to evaluate whether alleviation of ER stress using the chemical chaperone tauroursodeoxycholic acid (TUDCA) can reduce early tumorigenesis, fibrosis, inflammation, and malignant phenotypes. Together, these studies aim to characterize ER stress-regulated transcriptional and cellular programs and to assess the therapeutic and preventive potential of targeting ER stress pathways in HCC. Approach - Model Systems: Project 1 (AMG-PERK inhibitor): Chemically induced mouse model of HCC, in vitro HCC cell lines, and patient-derived organoids were used to assess the impact of PERK inhibition across multiple biological contexts. Project 2 (TUDCA inhibitor): Chemically induced mouse model of HCC and in vitro HCC cell lines were used to evaluate the effects of ER stress inhibition on tumor development, fibrosis, inflammation, and tumor progression. - Pharmacologic Intervention: Project 1: Use of the selective PERK inhibitor AMG-PERK 44 to assess its effects on PERK signaling, tumor behavior, and tumor–stroma interactions. Project 2: Treatment with tauroursodeoxycholic acid (TUDCA), a liver-derived bile acid conjugate known to reduce ER stress signaling, to alleviate ER stress and limit early hepatocarcinogenesis. - Mechanistic Studies: Project 1: Investigate the role of GP73 as a mediator of ER stress–dependent tumor–stroma communication, and examine its interaction with extracellular GRP78 and downstream PERK–CHOP signaling in hepatic stellate cells. Project 2: Assess how TUDCA modulates UPR sensors and downstream fibrogenic, proinflammatory, and EMT pathways in liver tissue and HCC cell lines. Molecular and Cellular Profiling: Project 1: Single-cell RNA sequencing, bulk transcriptomics, and immunohistochemistry to identify cell populations expressing PERK, GP73, and GRP78 (BiP), and to characterize ER stress–associated transcriptional programs, including proliferation, EMT, and inflammation. Project 2: Gene expression analyses and immunohistochemistry to measure UPR sensor expression, EMT markers, fibrosis, and inflammatory markers in liver tissue and HCC cell lines following exposure to TUDCA treatment. Thu, 05 Mar 2026 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30846731 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30846731 Uppsala universitet Jaafar Khaled Maria Kopsida Tania Payo Serafin Sofi Sennefelt Nyman Fredrik Rorsman Charlotte Ebeling Barbier Hans Lennernäs Markus Sjöblom Femke Heindryckx SummaryIn this study, we systematically compared two single cell whole genome sequencing (scWGS) kits in the MDA-based REPLI-g Single Cell Kit from Qiagen and the PTA-based ResolveDNA Whole Genome Amplification Kit from BioSkryb. The kits were evaluated across several key metrics, including amplification bias and coverage uniformity and copy number variant (CNV) calling. MethodsCultured MM.1S cells (a multiple myeloma cell-line) were incubated with propidium iodide (PI) and through Fluorescence-Activated Cell Sorting (FACS) sorted using the BD FACSAria IIu. Single or multiple cells were deposited using single cell mode into 96-well plates containing the recommended buffer for the Qiagen Repli-G MDA and BioSkryb ResolveDNA PTA kits. Cell sorting was performed on the same occasion and from the same sample of MM1.S cells to ensure minimal sample variation. For each kit, 26 wells with a single cells and two wells with 10 cells were FACS sorted into the 96-well plate. In addition, two wells with a mini-bulk of 10 cells, two positive controls wells with 1ng of genomic DNA (provided with the BioyoSkryb kit) in buffer and two empty wells (negative control containing only with buffer) were also included in each plate. Whole Genome Amplification (WGA) was performed using Multiple Displacement Amplification (MDA) using the Repli-G kit (Qiagen, Cat no: 1124559, 180318 and 1124693) or Primary Template-directed Amplification (PTA) using the ResolveDNA kit (BioSkryb, PN: 100136, 100691 and 100940) was performed according to the manufacturer’s instructions. After amplificaiton, ten random single wells, one mini-bulk and one positive control well were further selected for library preparation, using the same BioSkryb or Qiagen kit and finally qPCR quantification prior to sequencing. Illumina sequencing data for the scWGS kit samples is available on ENA under the study accession PRJEB97702. The same WGA DNA was also subjected to genotyping using the Illumina Infinium Global Screening Array v3.0 (Illumina). Arrays were processed on an Illumina iScan system according to the manufacturer's instructions. Initial data processing and genotype calling were performed in GenomeStudio (Illumina, v2.0.3), using Human Reference Genome build 38 (GRCh38) as reference. Genotyping details - Number of analyzed markers: 650 181 - Number of analyzed samples or individuals: 22 - Organism: Homo sapiens The genotyping was performed using the Illumina Infinium assay and the results were analyzed using the software GenomeStudio 2.0.3. - BeadChip type: GSA-24v3-0_A2 - Manifest file: GSA-24v3-0_A2.bpm - Genome build version: 38 - Cluster file ICF: GSA-24v3-0_A1_ClusterFile.egt ICF (Illumina Cluster File) result files represent genotyping data generated using cluster definitions established by Illumina from a reference DNA sample set. These reference-based cluster files are used when project-specific cluster files (PCF) are not available—typically in projects with fewer than 100 samples, where creating reliable project-based clusters is not feasible. In such cases, Illumina recommends using ICF files as they provide standardized clustering information derived from a larger, validated reference dataset, ensuring consistent genotype calling across experiments. The result folders and files have the extension name ̈PLUS ̈. This refers to that the file is based on genotype data exported from PLUS DNA strand according to the Human Reference Genome build38, as given in the marker file GSA-24v3-0_A2.csv. Contents Content are compressed using 7-Zip Folder: XK-4162_250519_ResultReport - Description of result report: XK-4162_250519_ResultReport.pdf - Manifest file: GSA-24v3-0_A2.csv - Cluster file: GSA-24v3-0_A1_ClusterFile.egt Folder: XK-4162_250519_ResultReport_ICF_PLUS - Statistics from the genotyping: XK-4162_250519_GenotypingStatistics_ICF_PLUS.xlsx - Genotype data: XK-4162_250519_SNPGenotypeExport_ICF_PLUS.txt - Results from HapMap comparison: XK-4162_250519_HapMap_1KG_Comparision_ICF_PLUS.xlsx - Identity test: XK-4162_250519_Identify_Item.xlsx - Gender test: XK-4162_250519_GenderEstimation.xlsx Folder: XK-4162_250519_PLINK_ICF_PLUS PLINK files - XK-4162_250519_PLINK_ICF_PLUS.map - XK-4162_250519_PLINK_ICF_PLUS.ped Folder: XK-4162_250519_IDAT - IDAT files - XK-4162_250519_Samplesheet.csv Tue, 28 Oct 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30084184 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-30084184 Kungliga Tekniska högskolan Pontus Höjer Abrahan Hernandez Michelle Ljungmark Robert Månsson Anja Mezger Julia Hauenstein Nicolai Frengen Jessica Nordlund Anastasios Glaros Eunkyoung Choi Susanne Björnerfeldt Camilla Enström This dataset includes single-cell RNA sequencing (scRNA-seq) data from co-cultures of primary T cells and the HNT-34 AML (acute myeloid leukemia) cell line after treatment with a SLAMF6 antibody or an isotype-matched control antibody. Libraries were produced using the 10X Genomics Chromium GEM-X Single Cell 5ʹ Reagent Kits v3 and sequenced on an Illumina Novaseq 6000 system (Illumina). The dataset is available as raw sequencing reads (fastq; restricted access) or as an annotated matrix of scRNA count data (h5ad). Published in: Sandén et al, Nature Cancer, 2025: https://www.nature.com/articles/s43018-025-01054-6 Tue, 01 Jul 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28033793 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28033793 Lunds universitet Carl Sandén Henrik Lilljebjörn Thoas Fioretos This dataset includes single-cell RNA sequencing (scRNA-seq) data from primary AML (acute myeloid leukemia) samples. Libraries were produced using the 10X Genomics Chromium Single Cell 3ʹ Reagent Kits v3 and sequenced on an Illumina Novaseq 6000 system (Illumina). The dataset is available as raw sequencing reads (fastq; restricted access) or as an annotated matrix of scRNA count data (h5ad). Published in: Sandén et al, Nature Cancer, 2025: https://www.nature.com/articles/s43018-025-01054-6 Tue, 01 Jul 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28263911 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28263911 Lunds universitet Carl Sandén Henrik Lilljebjörn Thoas Fioretos This dataset includes RNA sequencing (RNA-seq) data from the HNT-34 AML (acute myeloid leukemia) cell line after knockout of the SLAMF6 gene by CRISPR/Cas9 (SLAMF6-KO) or mock-knockout with a construct targeting the firefly luciferase gene (SLAMF6-WT). Libraries were produced using the Illumina stranded mRNA prep kit and sequenced on an Illumina Novaseq 6000 system (Illumina). The dataset is available as merged transcripts per million (TPM) data for all cases generated using Salmon (salmon.merged.gene_tpm.tsv.gz). Raw sequencing reads (fastq) are available at the European Nucleotide Archive (ENA) under accession ID PRJEB90909: https://www.ebi.ac.uk/ena/browser/view/PRJEB90909. Published in: Sandén et al, Nature Cancer, 2025: https://www.nature.com/articles/s43018-025-01054-6 Mon, 30 Jun 2025 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28033754 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-28033754 Lunds universitet Carl Sandén Henrik Lilljebjörn Thoas Fioretos Backgroud information The dataset contains single cell targeted RNA sequencing (RNAseq) and targeted antibody-oligonucleotide conjugates sequencing (Abseq) data from chimeric antigen receptor (CAR)-engineered T cells used to treat each individual cancer patients in a clinical study. The starting material was in all cases autologous T cells harvested from peripheral blood of patients. The data is collected from 24 participants of which 23 were adult patients with relapsed or refractory B cell lymphoma and one was a pediatric patient with relapsed B cell acute lymphoblastic leukemia. The data were generated as part of a study by Sarén et. al, Clinical Cancer Research (2023). Targeted RNA and protein single-cell libraries were generated using the BD Rhapsody™ platform (BD Biosciences). Cells were labeled with sample tags from the BD Human Immune Single-Cell Multiplexing Kit and BD Ab-seq Ab-Oligos and live cells were collected by flow cytometry. CAR-T cells were loaded on BD Rhapsody cartridge and mRNA captured with cell capture beads and used as template for cDNA synthesis. Four separate targeted libraries were produced and pooled for paired-end sequencing on NovaSeq 6000 S1 sequencer (Illumina) at the SNP&SEQ Technology Platform (Uppsala, Sweden). Terms of access Sequencing data generated during the current study are not publicly available due to the European General Data Protection Regulation (GDPR) to protect patients’ privacy but are available from the corresponding author on reasonable request (see contact info). The dataset is only to be used for research that is seeking to advance the understanding of CAR-T cell treatment of cancer. Ancillary datasets and code Processed RNAseq and AbSeq data, in the form of raw and normalized count matrices, are available on BioStudies (Accession: E-MTAB-12407). R code used to process the data is available on the study GitHub repository:https://github.com/magnessa/EudraCT_2016-004043-36 Wed, 02 Aug 2023 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-20208764 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-20208764 Uppsala universitet Claudio Mirabello Magnus Essand Mohanraj Ramachandran Tina Sarén Human breast cancer OMICs data generated for the publication "Solid phase capture and profiling of open chromatin by spatial ATAC" Abstract from the publication: Current methods for epigenomic profiling are limited in the ability to obtain genome wide information with spatial resolution. Here we introduce spatial ATAC, a method that integrates transposase-accessible chromatin profiling in tissue sections with barcoded solid-phase capture to perform spatially resolved epigenomics. We show that spatial ATAC enables the discovery of the regulatory programs underlying spatial gene expression during mouse organogenesis, lineage differentiation and in human pathology. Dataset description The dataset includes spatially-resolved chromatin accessibility profiling performed on three fresh-frozen tissue sections of HER2+ breast cancer. We provide raw data in the form of fastq files, along with processed feature barcode matrices, metadata, and photomicrographs of the tissue slices. Additionally the dataset contains spatially-resolved gene expression profiling of tissue sections from the same specimen. For this too, we provide raw and processed data, along with the metadata information. Spatial transcriptomics data were generated using 10X Genomics' Visium platform, while spatial ATAC data were created using a method introduced in our publication, which relies on an analogous workflow. Samples were sequenced on Illumina Nextseq 550 or 2000 and raw data were processed with CellRanger Gene Expression or ATAC-seq pipelines. To apply for conditional access to the dataset, please contact datacentre@scilifelab.se (mailto:datacentre@scilifelab.se) . Tue, 01 Nov 2022 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-21378279 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-21378279 Karolinska Institutet Margherita Zamboni Enric Llorens Bobadilla Xinsong Chen Johan Hartman This repository contains 10x Chromium linked-read WGS (lrWGS), RNAseq and H3K27Ac ChIPseq from multiple myeloma. The data consists of fastq files from lrWGS of 37 individuals with data from tumor and matched normal tissue from 32 of them. Additionally, it contains fastq files from RNAseq of 32 of the 37 patients and H3K27Ac ChIPseq data from select patients. The data set contains sensitive human genomic data and is under restricted access. Request for access can be made to datacentre@scilifelab.se. (mailto:datacentre@scilifelab.se) Tue, 14 Jun 2022 00:00:00 GMT https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-17049059 https://researchdata.se/sv/catalogue/dataset/doi-10-17044-scilifelab-17049059 Karolinska Institutet Lucia Pena-Perez Robert Månsson