11768 results for "cancer rna-seq"

in 20.27 milliseconds.

• Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Genome-wide cell-free DNA mutational integration enables ultra-sensitive cancer monitoring

  Dataset EGAD00001006237

• SNP data for Breast cancer PRS

  SNP data for 313 loci required for calculation of the Breast cancer PRS

  Dataset EGAD00001008144

• Lactate metabolism in cancer stem cell fate regulation

  Tumors arise as a result of uncontrolled cell proliferation driven by mutations in the signaling pathways that regulate the balance between cell division and differentiation. Despite of that, in the tumor, cancer stem cells seem to retain their capability to differentiate, which can lead to differential chemotherapy response. We previously showed that intestinal stem cells and differentiated cells display specific metabolic features and that they interact through metabolites. Moreover, we found that altering mitochondrial metabolism is sufficient to lead and define stem cell differentiation. In cancer, cellular metabolism is highly derailed. However, how metabolic changes in the cancer cell contribute to cell fate decisions remains unknown. Recently, the crosstalk between metabolism and epigenetics has emerged as one of the players in tumor development. Here we aim to address the impact of the altered metabolism of cancer cells on their epigenome as a regulatory axis of cell identity and fate. We use human-derived tumor organoids where we introduce genetic encoded fluorescent-based reporters to visualize cell identity and metabolism at single cell level. We combine 4D live imaging with a recently developed machine learning method to re-construct cell-lineages, trace stem cell state and metabolic changes during tumor organoid development. We find that, mimicking the tumor metabolic microenvironment (Warburg effect-driven low-glucose high-lactate) leads to changes cellular metabolism and histone acetylation. Strikingly, these alterations provoke profound changes in tumor development by increasing the population of cancer stem cells and their proliferation and differentiation dynamics.

  Study EGAS50000000063

• Coding and regulatory somatic profiling of triple-negative breast cancer in Sub-Saharan African patients

  The burden of triple-negative breast cancer (TNBC) may be shaped by genetic factors, particularly inherited and somatic mutation profiles. However, data on this topic remain limited, especially for the African continent, where a higher TNBC incidence is observed. In the age of precision medicine, cataloguing TNBC diversity in African patients becomes imperative. We performed whole exome sequencing, including untranslated regions, on 30 samples from Angola and Cape Verde, which allowed to ascertain on potential regulatory mutations in TNBC for the first time. A high somatic burden was observed for the African cohort, with 86% of variants being so far unreported. Recurring to predictive functional algorithms, 17% of the somatic single nucleotide variants were predicted to be deleterious at the protein level, and 20% overlapped with candidate cis-regulatory elements controlling gene expression. Several of these somatic functionally-impactful mutations and copy number variation (mainly in 1q, 8q, 6 and 10p) occur in known BC- and all cancer-driver genes, enriched for several cancer mechanisms, including response to radiation and related DNA repair mechanisms. TP53 is the top of these known BC-driver genes, but our results identified possible novel TNBC driver genes that may play a main role in the African context, as TTN, CEACAM7, DEFB132, COPZ2 and GAS1. These findings emphasize the need to expand cancer omics screenings across the African continent, the region of the globe with highest genomic diversity, accelerating the discovery of new somatic mutations and cancer-related pathways.

  Study EGAS50000001013

• Target sequencing of 27 cancer-predisposing genes and 13 renal cell carcinoma-related genes in Japanese renal cell carcinoma patients

  Identifying causative genes via genetic testing is useful for the screening, prevention, and treatment of cancer. Several hereditary syndromes are known to occur in renal cell carcinoma (RCC). However, these evidences came from the European population; it remains unclear how the RCC-related genes and other cancer-predisposing genes contribute to the development RCC in the Japanese population. A case-control study of 14 RCC-related genes and 26 cancer-predisposing genes was performed using 1,563 Japanese patients with RCC and 6,016 controls. Patients were divided into two major histological subtypes of clear cell RCC (ccRCC) or non-clear cell RCC (nccRCC). Gene-based analysis of germline pathogenic variants in patients with each subtype and cancer-free subjects was performed. Following quality control, 1,532 patients with RCC and 5,996 controls were further analyzed. For ccRCC, 52 of 1,283 (4.05%) patients carried pathogenic variants mainly in the cancer-predisposing genes such as TP53 (P = 1.73 �� 10-4; OR, 5.8; 95% CI, 2.2���15.7). Approximately 80% of patients with pathogenic variants in TP53 had p.Ala189Val that was specific in East Asian population. For nccRCC, 14 of 249 (5.62%) patients carried pathogenic variants mainly in the RCC-related genes such as BAP1 (P = 6.27 �� 10-5; OR, Inf; 95% CI, 10.0���Inf), and FH (P = 6.27 �� 10-5; OR, Inf; 95% CI, 10.0���Inf). Our study showed different and population-specific contributions of risk genes between ccRCC and nccRCC in Japanese for better-personalized medicine.

  Study JGAS000414

• A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  A Comprehensive Catalogue of Somatic Mutations from a Human Cancer Genome

  Dataset EGAD00001000149

• Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Whole genome bisulfite sequencing of prostate cancer samples upon oral pimonidazole administration

  Dataset EGAD50000000099

• APOBEC Breast Cancer Whole Genome

  Five tumor-normal human breast cancer cases driven by APOBEC mutagenesis, including normal tissue, primary, and metastatic samples for each patient.

  Dataset EGAD50000001275

• SCLC tumor sequencing

  In this study we have sequenced the tumor of 28 Small Cell Lung Cancer patients.

  Study EGAS00001003985

• Single_cell_RNA_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008271

• SNP data for Ovarian cancer PRS (controls)

  SNP data of 28 sites required for the Ovarian cancer PRS (controls)

  Dataset EGAD00001008145

• Single Cell RNAseq of blood and tumor from renal cancer patients

  Single Cell RNAseq of blood and tumor from renal cancer patients

  Dataset EGAD00001005977

• Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Clinical Implications of Genomic Alterations in the Tumour and Circulation of Pancreatic Cancer Patients

  Dataset EGAD00001001421

• WES data of serially passaged TIC-enriched spheres of colorectal cancer

  WES data of serially passaged TIC-enriched spheres of colorectal cancer (CRC)

  Dataset EGAD00001006266

• The genetic evolution of precursor lesions in pancreatic cancer

  Mapping genetic evolution of pancreatic cancer precursor lesions such as IPMNs and PanINs.

  Dataset EGAD00001002232

• Analysis of Recurrently Protected Genomic Regions in Urine Cell-Free DNA

  This study investigated fragmentation patterns in urine and plasma cell-free DNA and evaluated the relevance of such analysis for cancer diagnostics using urine samples. Specifically, we performed whole-genome sequencing at variable depths and evaluated genome-wide coverage in urine and plasma samples, relative to known genome annotation features. Tumor/germline exome sequencing data included was used to identify genome-wide copy number changes and their effect on cfDNA fragmentation in corresponding genomic regions in urine samples. Data included in this submission is whole-genome sequencing of urine and/or plasma cell-free DNA from healthy volunteers and/or cancer patients, and whole-exome sequencing of tumor DNA and germline DNA (peripheral blood cells) from cancer patients. 

  Study phs002273

• DAC for Early detection of ovarian cancer using cell-free DNA fragmentomes and protein biomarkers

  Ovarian cancer is a leading cause of death from gynecological malignancies worldwide. No effective screening methods exist resulting in tumor detection at advanced stages where treatment is less effective. In this study, we used whole-genome cell-free DNA (cfDNA) fragmentome and protein biomarker (CA-125 and HE4) analyses to evaluate 591 women, mostly post-menopausal, from the European Union or the United States with ovarian cancer, benign adnexal masses, or without ovarian lesions. Using a machine learning model that incorporated multi-analyte fragmentome data and protein measurements, we evaluated the detection of ovarian cancer in two clinical settings, detection of ovarian cancers among a healthy population, and classification of adnexal masses as benign or malignant. The combined approach detected ovarian cancer with specificity >99% and sensitivity of 72%, 69%, 87%, and 100% for stages I–IV, respectively (AUC=0.96, 95% CI: 0.94-0.99), including 90% of high grade serous ovarian cancers. At the same specificity, CA-125 alone detected 34%, 62%, 63%, and 100% of ovarian cancers for stages I–IV (p=0.001, two-sided test of equal proportions). Additionally, our approach differentiated benign masses from ovarian cancers with high accuracy (AUC of 0.88, 95% CI=0.83-0.92). These results were validated in an independent population. These findings show that integrated cfDNA fragmentome and protein analyses detect ovarian cancers with high performance, enabling a new accessible approach for noninvasive ovarian cancer screening and diagnostic evaluation.

  Dac EGAC50000000332

• Genome-wide NanoRCS sequencing of cfDNA and ctDNA from liquid biopsies of healthy individuals and cancer patients

  Levels of circulating tumor DNA (ctDNA) in liquid biopsies may serve as a sensitive biomarker for real-time, minimally-invasive tumor diagnostics and monitoring. However, detecting ctDNA is challenging, as much fewer than 5% of the cell-free DNA in the blood typically originates from the tumor. To detect lowly abundant molecules, extremely sensitive sequencing methods are required. We developed a new technique, NanoRCS, which is based on Oxford Nanopore sequencing of concatenated copies of a single DNA molecule for genome-wide sequencing, and applied this to plasma of healthy individuals, Esophageal cancer patients and Granulosa cell tumor patients and ascites of Ovarian cancer patients. We also sequenced these samples on Illumina Novaseq, to compare the two techniques.

  Study EGAS50000000154

• Sensitive circulating tumor DNA based residual disease detection in epithelial ovarian cancer

  In this study we implemented a circulating tumor DNA (ctDNA) based blood test for more accurate post-operative surveillance of this disease. We analyzed 264 plasma samples collected between June 2016 and September 2021 from 63 epithelial ovarian cancer patients using tumor-guided plasma cell-free DNA analysis to detect residual disease after treatment. Assay specificity was verified using cross-patient analysis of 1195 control samples.

  Study EGAS50000000245

• Study assessing the efficacy and safety of durvalumab plus olaparib plus fulvestrant in selected metastatic or locally advanced ER-positive, HER2-negative breast cancer patients.

  This study aims to evaluate the efficacy of the combination of olaparib, durvalumab, and fulvestrant for the treatment of patients with ER-positive, HER2-negative, locally advanced or metastatic breast cancer with BRCA gene alterations or alterations of genes involved in HRR or MSI status, in terms of the progression-free survival rate (PFSR) at 24 weeks.

  Study EGAS50000001166

• Cancer_Genome_Project_Exome_Sequencing

  Agilent whole exome hybridisation capture was performed on genomic DNA derived from cancer and matched normal DNA from the same patients. Next Generation sequencing performed on the resulting exome libraries and mapped to build 37 of the human reference genome to facilitate the identification of novel cancer genes. Now we aim to re find and validate the findings of those exome libraries using bespoke pulldown methods and sequencing the products.

  Study EGAS00001000301

• SDH_deficient_renal_tumours___RNA_

  Succinate dehydrogenase (SDH) deficient renal cancers are a rare and understudied subtype of renal cancer. As such they represent an unmet need in kidney cancer research. Here, we seek to describe the genomics and transcriptomics of SDH deficient renal cancers by sequencing five such tumours. We document their mutational burden, define driver mutations, demonstrate typical patterns of structural variants, and estimate the cell of origin and cellular composition.

  Study EGAS00001004103

• Spontaneous mutations in the single TTN gene represent high tumor mutation burden

  Tumor mutation burden (TMB) is an emerging biomarker, whose calculation requires targeted sequencing of many genes. We investigated if the measurement of mutation counts within a single gene is representative of TMB. Whole exome sequencing (WES) data from the pan-cancer cohort (n=10,224) of TCGA, and targeted sequencing (tNGS) and TTN gene sequencing from 24 colorectal cancer samples (AMC cohort) were analyzed.

  Study EGAS00001004009

• Sequence data from stage I of the CCTG BR.36 ctDNA-directed, multi-center phase II study of molecular response adaptive immunotherapy in non-small cell lung cancer

  The dataset for the study "Early ctDNA molecular response captures therapeutic response in the first stage of CCTG BR.36 ctDNA-directed, multi-center phase II study of molecular response adaptive immunotherapy in non-small cell lung cancer", includes 134 bam files from hybrid capture targeted error-correction next-generation sequencing (PGDx Elio plasma resolve) from plasma cell-free DNA and matched white blood cell DNA from 35 individuals with non-small cell lung cancer on the BR.36 trial, alongside 11 bam files from targeted next generation sequencing (PGDx Elio tissue complete) of tumor DNA from 11 individuals with non-small cell lung cancer on the BR.36 trial.

  Dataset EGAD00001011359

• Integrative Gene Regulatory Network Analysis Discloses Key Driver Genes of Fibromuscular Dysplasia

  Fibromuscular dysplasia (FMD) is a poorly understood vascular disease that can lead to hypertension, stroke, myocardial infarction and even death. In 2013 we initiated the DEFINE study (ClinicalTrials.gov Identifier: NCT01967511), which has now recruited > 440 subjects and which aims to unravel the pathobiological mechanisms of this disease. Here, by integrating DNA genotype and RNA sequence data from primary fibroblasts of 83 FMD patients and 71 matched healthy controls from the DEFINE study, and its precursor the CAUSE study, we inferred 18 gene regulatory co-expression networks, four of which were found to act together as an FMD-associated super network in the arterial wall. Molecular studies indicated that this super network, termed SN-A and with the top key driver gene UBR4, governs multiple aspects of vascular cell physiology and functionality, including collagen/matrix production. The effects of UBR4 knockdown were also investigated in commercially available aortic smooth muscle and fibroblast cell lines by RNA sequencing, and these results are included in these datasets.Note, that of the 154 subjects in this analysis, 62 did not provide consent to have their genomic data made publicly available. Therefore the human subject data available here is from the 92 study participants who provided written informed consent to share their deidentified genomic data publicly.

  Study phs003674

• The_Genomic_Advances_in_Sepsis__GAinS__RNA_seq

  Sepsis is defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. This cohort comprises a subset of patients enrolled in the Genomic Advances in Sepsis (GAinS) study, an established biobank of adult sepsis patients. Patients with sepsis due to community acquired pneumonia or faecal peritonitis were recruited from 34 hospitals across the UK from 2005-2018, with samples for functional genomics and detailed clinical information collected on the first, third and/or fifth day following ICU admission. RNA was extracted from leukocytes isolated at the bedside using LeukoLOCK kits. We have previously identified sepsis response signatures (SRSs), transcriptomic endotypes that are associated with differential early mortality (Davenport et al, Lancet Respir Med, 2016; Burnham et al, AJRCCM, 2017) and response to treatment in a clinical trial (Antcliffe et al, AJRCCM, 2018). We generated RNA sequencing data on 903 samples, including 134 samples repeated from our previously released microarray data. Libraries were prepared using NEB Ultra II Library Prep kits (Illumina) and sequenced on a NovaSeq 6000. Reads were aligned to the reference genome (GRCh38) using STAR and gene counts quantified using featureCounts (annotation Ensembl v99). Counts were TMM-normalised and log-transformed. Following QC, processed data were available on 864 samples from 667 unique patients.

  Study EGAS00001003772

• Host pathogen interaction long read transcriptome

  Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular in transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are predicted to be benign using the reference transcript annotation. In further support of SUsPECT’s utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

  Study EGAS00001006779

• ALK inhibitors in the context of ALK-dependent cancer cell lines

  ALK inhibitors in the context of ALK-dependent cancer cell lines

  Dataset EGAD00001000078

• Matched breast cancer fusion gene study

  Matched breast cancer fusion gene study

  Dataset EGAD00001000097

• Genomic Alterations in Gingivo-buccal Cancer: ICGC-India Project_YR01

  Genomic Alterations in Gingivo-buccal Cancer: ICGC-India Project_YR01

  Dataset EGAD00001000272

• Sequencing data for Australian Ovarian Cancer study submitted 20121116

  Sequencing data for Australian Ovarian Cancer study submitted 20121116

  Dataset EGAD00001000293

• Sequencing data for Australian Pancreatic Cancer study submitted 20130102

  Sequencing data for Australian Pancreatic Cancer study submitted 20130102

  Dataset EGAD00001000323

• Early ctDNA molecular response captures therapeutic response in the first stage of CCTG BR.36 ctDNA-directed, multi-center phase II study of molecular response adaptive immunotherapy in non-small cell lung cancer

  Study EGAS00001007298

• Large scale familial CRC exome sequencing study

  The burden of rare disruptive mutations to the heritable risk of colorectal cancer

  Study EGAS00001001666

• Whole_Genome_sequencing_of_rhabdomyosarcoma_tumour_tissue

  The aim of this study is to investigate the genomic landscape of human cancer.

  Study EGAS00001008277

• TP53 in ovarian cancer panel aligned reads Data Access Committee

  TP53 targeted panel aligned reads consisting of BAM paired end reads from ovarian cancer tumor samples Data Access Committee

  Dataset EGAD00001003119

• Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Geographic variation of mutagenic exposures in kidney cancer genomes – sequence data (Mutographs)

  Dataset EGAD00001012102

• panel data of ALK-positiv non-small cell lung cancer patients

  270 samples with ALK-positiv non-small cell lung cancer, targeted sequencing (198 kb panel size)

  Dataset EGAD00001006297

• Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer

  The evolution of cancers from low grade to metastatic disease is a major determinant of cancer mortality. Cancer evolution involves a complex interplay between cell intrinsic genetics and transcriptional alterations and the extrinsic microenvironment. To define the key mechanisms underpinning metastatic development, we focused on the most lethal form of prostate cancer, metastatic castration-resistant prostate cancer, and employed single-cell multi-omics and whole-genome sequencing to deeply profile 34 metastatic lesions from 9 patients obtained from rapid autopsy. We found that intra-tumour heterogeneity is an indicator of key evolutionary processes, characterised by recurrent tumour populations acting as critical functional components of the tumour ecosystem, irrespective of clonal and microenvironmental backgrounds. Unexpectedly, microenvironments across metastatic sites played a limited role while clonal evolution primarily promoted transcriptional noise. Intra-patient functional convergence of tumour ecosystems was observed across metastatic sites, pointing towards system-level selection pressures that drive the heterogeneity landscape of mCRPC. Our findings reveal functional evolutionary convergence of metastatic disease into units of intra-tumour heterogeneity identifying critical determinants of metastatic progression for therapeutic targeting. Note: matched WGS for some tumour single-cell experiments, as well as germline WGS for patients CA0027, CA0034, CA0035, CA0043, and CA0076, can be found in EGAS00001006598.

  Study EGAS50000001312

• Quantitative analysis of a novel DNA hypermethylation panel for lung cancer diagnosis using bronchial specimens

  Non-diagnostic findings in Transbronchial Lung Biopsy (TBLB) and Endobronchial Ultrasound-guided Transbronchial Lung Biopsy (EBUS-TBLB) are not uncommon. A challenge is to improve lung cancer detection by TBLB/EBUS-TBLB. In order to improve the diagnostic yield of bronchoscopy, we used 850K methylation chip to identify methylation sites that distinguish malignant from benign lung nodules.We found that the combination of HOXA7, SHOX2,and SCT methylation analysis has the best diagnostic yield in 123 bronchial washing (sensitivity: 74·1%; AUC: 0·851) and 172 brushing samples (sensitivity: 86·1%; AUC: 0·915). We developed a kit comprising these three genes and validated the kit in 329 unique bronchial washing samples, 397 unique brushing samples, and 179 unique patients with both washing and brushing samples. The diagnostic accuracy of the panel alone in lung cancer diagnosis was 86·9%, 91·2% and 95% in bronchial washing, brushing, and washing+brushing samples, respectively. The panel's sensitivity in combination with cytology, rapid on-site evaluation (ROSE), and histology in lung cancer diagnosis was 90·8% and 95.8% in bronchial washing and brushing samples, respectively, and 100% in washing+brushing samples. Quantitative analysis of the three-gene panel improves lung cancer diagnosis by bronchoscopy.

  Study EGAS00001007089

• Rapid identification of somatic genome rearrangements as personalized biomarkers for blood-based cancer monitoring

  Increasing evidence shows the value of circulating tumour DNA (ctDNA) to detect cancer and monitor its progression. Somatic genomic structural variations (SVs) are promising personalized biomarkers for sensitive and specific detection of ctDNA in liquid biopsies. However, accurate, affordable, and fast identification of such SV biomarkers is challenging, which hinders routine use in the clinic. Here, we demonstrate a novel approach - termed SHARC - for rapid discovery of somatic SV breakpoints as personalized tumour biomarkers. SHARC combines low-coverage cancer genome sketching by using Oxford Nanopore portable sequencing with a random forest classification and a dedicated filtering pipeline to enrich for somatic SVs. Our method leverages the real-time and long-read capabilities of Nanopore sequencing to identify somatic SV breakpoints at nucleotide resolution from a tumour biopsy within two days. We applied SHARC to tumour samples of high-grade ovarian and prostate cancer and validated on average 10 somatic SVs per sample with the use of PCR mini-amplicons. Finally, we demonstrate that these somatic SV biomarkers can be used to detect tumour presence from liquid biopsies in a quantitative manner and we retrospectively monitored treatment response in patients with prostate cancer, demonstrating its potential benefit for clinical practice.

  Study EGAS00001003963

• Genomic Alterations in Gingivo-buccal Cancer: ICGC-India Project_YR02

  As a part of the ICGC, India has undertaken genomic studies on gingivobuccal cancer of the oral cavity, which is the most prevalent form of cancer among men in India. There are various known environmental (life-style) correlates of this cancer, the most important of which are tobacco chewing and HPV infection. Paired DNA samples – isolated from the tumour tissue and from the blood of fifty six patients – have been analyzed to catalog germline and somatic mutations. Association between each observed genomic alteration and exposure to environmental risk factors is being explored.Detailed clinical characterization of the patients, collection of data on demographic and environmental exposures, and isolation of DNA samples from blood and tumour tissues collected from each patient are being done at the Advanced Centre for Research, Treatment and Education on Cancer, Mumbai. Exome capture and deep resequencing are being performed at the National Institute of Biomedical Genomics, Kalyani. We have analyzed the exomes of paired blood and tumour DNA samples using exome capture by Illumina TrueSeq Exome Enrichment kit followed by sequencing on Illumina HiSeq-2000. Each exome is being sequenced at a mean depth of 70x for Tumour and 40x for Blood. In addition, we have used Illumina Omni 2.5 SNP-chips to generate genotype data.

  Study EGAS00001001028

• LLDeep DAG2+ scRNA-seq in PBMCs

  Single cell transcriptomics of PBMCs of 47 donors from the Lifelines Deep cohort (general population, Northern part of the Netherlands). Cells of five or six different donors were pooled together in one sample pool, resulting in eight different sample pools. In total, 28.855 cells were captured and their transcriptomes were sequenced to an average depth of 74k. Genotype data was available for each donor, which allowed us to use the Demuxlet method that uses variable SNPs between the pooled individuals to determine which cell belongs to which individual. Since genotype information is lacking of 2 individuals, the transcriptome of only 45 individuals could be retrieved.

  Dataset EGAD00010001315

• Samples obtained within X-pand project

  This dataset contains five bone marrow samples and three mobilised peripheral blood ones from different healthy individuals. The three bone marrow samples have been sequence with the 10x multiome kit using Illumina sequencers. The three mobilised peripheral blood ones have been split in two aliquots, one for 10x multiome and the other for 10x CITE-seq. Both libraries types have been sequenced with an Illumina sequencer. Raw data has been processed with cell-ranger and map to the human genome (GRCh38) to obtain the bam files.

  Dataset EGAD50000001108

• scRNAseq: Human liver cholangiocyte organoids capture the heterogeneity of in vivo human liver ductal epithelium

  Liver ductal organoids expanded in either conventional (Huch et al., 2015) or refined medium were split and seeded either in the same medium or in DM+. They were sequenced on day 10 after splitting. Droplet-based scRNA-seq was performed using the 10x Genomics Chromium Single Cell Kit v3, and libraries were sequenced on an Illumina NovaSeq 6000 S4. Raw sequencing data were processed using Cell Ranger software (v6.0.1) with the human genome reference (hg38) and gene annotation (Ensembl 98). Dataset integration and downstream analysis were mainly performed using Seurat (v4.3.0.1) with R v4.1.3 “One Push-up”.

  Dataset EGAD50000001464

• scRNA-seq to study interactions between HSPCs, BMSCs and immune microenvironment

  Mechanisms of clonal evolution in myeloid neoplasms remain incompletely understood. Darwinian theory predicts that the (micro)environment of clone-propagating stem cells may contribute to clonal selection. Here, we provide data fitting this model, establishing a relationship between stromal niche inflammation, inflammatory stress in HSPCs, clonal resistance and leukemic evolution in human MDS.

  Study EGAS00001008181

• ATAC-seq of uncultured CD112high and CD112low long-term(LT) human hematopoietic stem cells (HSC) from umbilical cord blood

  ATAC-sequencing was performed to establish chromatin accessibility signatures that underlie the differential in vitro priming and regeneration phenotypes of CD112high and CD112low LT-HSC. CD112high and CD112low LT-HSC from 2-3 independent human umbilical cord blood pools were prospectively isolated for DNA extraction, library preparation and sequencing.

  Study EGAS00001005121

• 112 "KOREAN" never-smoker female adenocarcinoma exome-seq

  All sequencing was performed within the DNAlink (Korea) by using the Solexa sequencing technology (Illumina, San Diego, CA). 1 ug of genomic DNA was sheared to an average size of 150 bp by using the Covaris System. The libraries were prepared by using TruSeq DNA Sample Prep Kit (Illumina). The purified DNA library was hybridized with the SureSelect Human All Exon V3 probe set (Agilent Technologies) to capture 50 Mb of targeted exons following the manufacturer’s instructions. Exome capture was carried out using the Agilent SureSelect Human All Exon 50Mb Kit. The captured exome libraries were sequenced on the Illumina HiSeq2000 using the manufacturer’s recommended protocols.

  Dataset EGAD00001005126

• Single-cell multi-omics analysis of COVID-19 patients with pre-existing autoimmune diseases

  Single-cell analysis of the transcriptome, T cell immune receptors, and surface proteome (CITE-seq) from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients with pre-existing autoimmune diseases (rheumatoid arthritis n = 5, psoriasis n = 4, or multiple sclerosis n = 3), as well as COVID-19 patients without pre-existing autoimmunity as controls (n = 10) to investigate altered immune responses.

  Dataset EGAD00001007982

• Single-cell T-cell receptor sequencing data of peripheral blood mononuclear cells obtained from 30 ATL patients, 11 HTLV-1-infected asymptomatic carriers, and 4 healthy donors.

  Single-cell T-cell receptor sequencing (scTCR-seq) data of peripheral blood mononuclear cells (PBMCs) obtained from 30 ATL patients (34 samples including 4 sequential ones), 11 HTLV-1-infected asymptomatic carriers, and 4 healthy donors.

  Dataset EGAD00001007014