11987 results for "cancer rna-seq"

in 22.98 milliseconds.

• Microinjection of hIPSC-derived intestinal organoids with Salmonella Typhimurium

  To generate an RNA-Seq dataset for organoids apically stimulated with Salmonella Typhimurium.These data are part of a pre-publication release. For information on the proper use of pre-publication data shared by the Wellcome Trust Sanger Institute (including details of any publication moratoria), please see http://www.sanger.ac.uk/datasharing/

  Dataset EGAD00001001363

• Single-Nuclei RNA-seq of healthy (post-mortem) and temporal lobe epilepsy (biopsy) subjects.

  To identify dysfunctional neuronal subtypes underlying seizure activity in the human brain, we have performed single-nucleus transcriptomics analysis of >110,000 neuronal transcriptomes derived from temporal cortex samples of multiple temporal lobe epilepsy and non-epileptic subjects.

  Dataset EGAD00001006575

• Locally advanced prostate cancer cohort

  Fastq files from RNA sequencing and BAM files from tNGS of normal and tumor samples from 17 patients from the locally advanced prostate cancer cohort.

  Dataset EGAD50000002380

• CD8+ Tumor-Infiltrating Lymphocyte Abundance is a Positive Prognostic Indicator in Nasopharyngeal Cancer

  This retrospective tissue study explores the prognostic significance of tumour-infiltrating lymphocyte (TIL) populations in NPC, using a combination of RNA sequencing and immunohistochemistry.

  Study EGAS00001006396

• Single-cell RNA sequencing on 12346 single T cells from 14 non-small cell lung cancer (NSCLC) patients

  Deep single-cell RNA sequencing data for 12346 T cells from tumour, adjacent normal tissue and peripheral blood of treatment-naïve NSCLC patients

  Dataset EGAD00001003999

• Single-Cell RNA-Seq Profiling of Peripheral Immune Responses to Severe Infection in Uganda

  We conducted a prospective observational study (RESERVE-U-2-TOR) of adults admitted to Tororo General Hospital in eastern Uganda with severe, undifferentiated infection (i.e., suspected sepsis). Peripheral blood mononuclear cells (PBMCs) were isolated from freshly collected heparinized whole-blood samples (4 - 5 mL) using a standard density-gradient protocol (STEMCELL Technologies). PBMCs were aliquoted, cryopreserved in CryoStor CS10 (STEMCELL Technologies), stored overnight at -80°C, and transferred to liquid nitrogen for longer-term storage. Upon thawing, viability was assessed using the Nexcelom Cellaca Cell Counter, and cells were diluted to optimal concentrations for loading onto the Chromium Controller (10X Genomics). Each sample targeted the capture of ~10,000 GEMs (gel bead-in-emulsions). Single-cell RNA libraries were prepared using the Chromium Single Cell 3' Reagent Kit v3.1 following the manufacturer's standard protocol. Libraries were quality-assessed using an Agilent TapeStation and quantified using both Qubit 2.0 Fluorometer (Invitrogen) and qPCR (Applied Biosystems). Sequencing was performed on the NovaSeq X Plus platform (Illumina) using a 28×91 read configuration per 10X guidelines, targeting ~50,000 reads per cell. Data from this study available through dbGaP include raw single-cell RNA-seq data generated from PBMC samples collected from a subset of RESERVE-U-2-TOR participants at study enrollment (N=8).

  Study phs004100

• Synergy study: "Tissue resident CD8+ T cell clonal expansion in advanced triple negative breast cancer is associated with response to chemoimmunotherapy"

  Dataset of 10x Genomics 5'prime single-cell RNA sequencing of 40 tumor biopsy samples or triple negative breast cancer. Samples are taken before and after immuno checkpoint blockade treatment. Processed according the 10X genomics protocol, sequenced on Immumina NovaSeq6000. Raw sequencing reads are further processed with Cellranger 3.0 to get gene-expression matrices.

  Study EGAS50000000527

• Single cell RNA sequencing of human embryonic forebrain after slice culturing for between four weeks and one month

  With the aim of verifying normal tissue development in our Organotypic Timelapse recording with Transcriptomic Readout (OTTR) live tissue imaging method, we performed single cell RNA sequencing in organotypic slice cultures containing labelled cells from virally delivered ubiquitous Enhanced yellow fluorescent protein (EYFP)-expression. Human fetal cortices of PCW 7.5-10.5 were cut into strips along the dorso-ventral axis and directly placed en face, ventricular side down, on PTFE culturing membranes. To assess tissue health with our culturing method, we performed single cell RNA-seq analysis of the cortical explants (using adjacent slices from the same samples subjected to timelapse imaging). We cultured these control slices for four days in vitro (mimicking the start of a live imaging experiment after lentiviral infection and expression of EYFP), 11 days in vitro (corresponding to the end of one week of live imaging), and for two weeks and one month in vitro.

  Study EGAS50000001185

• Characterization of High-Grade Serous Ovarian Cancer Subtypes via Single-Cell and Spatial-Transcriptomics Profiling

  We measured gene expression in single-cell RNA sequencing samples from patients with high-grade serous ovarian cancer (HGSOC), for a study on improving HGSOC subtype definition by taking into account varying cell type proportions within tumors. The initial dataset (added October, 2020) contains 3 samples (3 individuals). The next dataset (added November, 2022) contains 34 samples from 8 individuals. The samples in the second dataset were sequenced in five different ways: 1) rRNA depletion and bulk RNA sequencing of tumor chunks, 2) rRNA depletion and bulk RNA sequencing of dissociated cells, 3) poly-A capture and bulk RNA sequencing of dissociated cells, 4) poly-A capture and scRNA sequencing of dissociated cells, 5) barcoding and pooling cells and scRNA sequencing (2 pools of 4 samples each). The third dataset (added June, 2024) contains data from one spatially resolved transcriptomics experiment (4 samples, one per Visium capture area, describing one tumor from one individual) and scRNA sequencing on 5 samples (5 individuals).

  Study phs002262

• The transition from quiescent to activated states in human hematopoietic stem cells is governed by dynamic 3D genome reorganization

  To elucidate the epigenetic changes which occur when human long-term hematopoietic stem cells (LT-HSC) become activated we performed Bulk ATAC-Seq on 13 sorted bulk hematopoietic populations from cord bloodas well as single-cell ATAC-Seq upon CD34+CD38-CD45RA- cells enriched for HSC as well as CD34+/CD38+ progenitor cells both from cord blood. These studies revealed gains of chromatin accessibility around CTCF binding sites during HSPC activation, as such we additionally performed Low-C to directly profile the 3D conformation of human cord-blood derived LT-HSC and Short-term hematopoietic stem cells (ST-HSC), as well as Hi-C , ATAC-Seq and CTCF ChIP-Seq upon the OCIAML-2 cell line in which CTCF sites gained during LT-HSC activation are enriched. Finally we transduced human cord-blood LT-HSC with an shCTCF vector; in-vitro cultured LT-HSC cells harbouring shCTCF were used to perform RNA-Seq, and scATAC-Seq was performed on CD34+/CD38- human CB cells transduced with shCTCF, four weeks post xeno-transplantation into mice. Collectively these studies have helped us demonstrate the role of 3D chromatin conformation changes during human LT-HSC activation.

  Dataset EGAD00001006447

• Upcycling and merging data to challenge the dogma and identify new therapeutic targets for Glioblastoma

  Glioblastoma is the most common brain tumour. Characterised by a poor prognosis and its recurrence after multimodal treatments, the search for preventable risk factors has been mainly inconclusive up to date. Recently, the data merge from datasetsdeposited at the EGA allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. Challenges in glioblastoma research As for virtually all cancer types, many efforts have been made to unveil the molecular features responsible of the disease, and several cellular pathways have indeed been identified as being frequently mutated in glioblastoma. Nonetheless, targeted therapies based on identified genes have so far failed to improve outcome, thus survival mostly relies on a standard treatment unchanged since 2005. This is a frustrating situation for both the scientific and medical communities, and above all for the patients, still facing a dreadful path. Some researchers hypothesised that this may be due to the inability to efficiently target cancer stem cells, the originators of the other cell types, thus inducing cancer relapse. In 2019 Charles P. Couturier and colleagues sequenced RNA from single cells of freshly excised glioblastomas of 16 patients, and demonstrated that glioblastoma cells replicate normal brain cell development with a conserved neural cancer cell hierarchy centered around glial progenitor-like cells. In this way, they helped identify the possible target cells to improve efficacy and durability of treatment. Data upcycling at the EGA: the glioblastoma case study Single cell RNA sequencing is generated with a laborious and expensive protocol. The quality of the starting material is crucial (cells sample freshly extracted from the patients) and often several attempts are needed before producing reliable quality sequencing results. Collecting big numbers of patients is also challenging. The sequencing data produced by Kevin Petrecca’s group in Montreal, Canada and deposited at the EGA (EGAS00001004422) was recently upcycled by Lin Wang in San Francisco, California and pooled with their freshly produced ones, and then deposited at the EGA as Dataset EGAS00001004909 The data merge allowed Aaron Diaz’s team to discover that the glioblastoma cells shift toward a mesenchymal phenotype when the tumour is recurring. They profiled 86 primary-recurrent patient-matched paired glioblastoma samples with single-nucleus RNA, among other techniques. These very comprehensive results lead the team to challenge the findings from several other cancer fields where chemotherapy standard chemo-radiation therapy selection pressure at the level of genomic alterations; this is indeed not the case for glioblastoma, where the pressure results in phenotypic transition between cellular states. Several technical controls were made to ensure that the merge of the data was not introducing a bias in the results, and an Inter-table analysis demonstrated nearly equal contribution to overall variance from each of the studies included, indicating that the findings were not due to inter-laboratory technical effect. A novel principal-component analysis showed that the largest contribution to variation in primary glioblastoma neoplastic cells was an axis between MES (mesenchymal) and proneural expression programs. In summary, treating glioblastoma often makes a MES, as commented by Lucy Stead about this remarkable work. Check Aaron Diaz’s team Nature Cancer paper, with strong technical tools, state of the art analysis and data from different sources converging to the same outcomes that made possible a significant step toward a better handling of a frightful cancer. References Couturier, C.P., Ayyadhury, S., Le, P.U. et al. Single-cell RNA-seq reveals that glioblastoma recapitulates a normal neurodevelopmental hierarchy. Nat Commun 11, 3406 (2020). Stead, L.F. Treating glioblastoma often makes a MES. Nat Cancer 3, 1446–1448 (2022). Wang, L., Jung, J., Babikir, H. et al. A single-cell atlas of glioblastoma evolution under therapy reveals cell-intrinsic and cell-extrinsic therapeutic targets. Nat Cancer 3, 1534–1552 (2022). Related links: Kevin Petrecca’s group dataset deposited at the EGA: EGAS00001004422 Lin Wang dataset deposited at the EGA: EGAS00001004909

  Blog upcycling-and-merging-data-to-challenge-glioblastoma

• Plasma cell‐Free transcriptome profiling in chronic liver disease

  Chronic liver disease (CLD) encompasses a wide range of hepatic disorders that progressively compromise liver function. In order to support the development of non-invasive diagnostic tools and improve patient stratification, we generated a high-throughput extracellular RNA sequencing dataset from blood plasma samples collected from patients at various stages of liver disease. Samples were obtained from two independent cohorts of 112 (cohort 1) and 489 (cohort 2) patients using standardized collection and processing protocols, and profiled using total RNA sequencing (SMARTer Stranded Total RNA-Seq Kit v3 - Pico Input Mammalian), resulting in 112 and 820 extracellular transcriptomic profiles for cohort 1 and 2, respectively. This dataset is provided with full documentation of the preprocessing analysis pipeline and accompanying metadata, enabling its use as an independent validation cohort for biomarker discovery and as a complementary resource for integrative multi-omics studies, even in cases with incomplete data modalities. The publicly available resource is intended to facilitate reproducibility and foster further research in the field of liver disease diagnostics

  Study EGAS50000000545

• Whole genome sequencing data of pediatric B-other subtype acute lymphoblastic leukemia

  For a long time, approximately 70% of childhood B-ALL cases could be classified routinely, and the remaining 30% were classified into the “B-other” group lacking the known genetic features. However, more recently, new genomic tools have uncovered new subtypes among the B-other groups. Approximately 10% of B-ALL cases lack the recurrent genetic abnormalities, and are referred as "B-other". They are typically more heterogeneous than other subtypes of B-ALL, and their prognoses are more variable. In a more recent update of the B-ALL classification, 27 subtypes were identified (Arber et al., 2022). The identification of some of these subtypes is dependent on NGS methods, such as RNA sequencing, which are not routinely used in all clinical laboratories. For example, ETV6::RUNX1-like and DUX4-rearranged subtypes have been identified using RNA sequencing. For this study, subtype predictions for some of the cases is acquired from ALLIUM (Krali et al., NPJ Precis Oncol., 2023) and ALLCatchR (Beder et al., Blood, 2022) based on the RNA-seq profiles.

  Study EGAS50000001497

• BAM files ChIP-Seq

  Data from the paper Context-specific Effects of TGFβ/SMAD3 in Cancer Are Modulated by the Epigenome. Tufegdzic et al, Cell Reports 2015

  Dataset EGAD00001001669

• Prostate cancer datasets WGS

  Low pass WGS: 48 samples (5 blood samples from 6 patient data): 22 Tumour cores and 26 normal/benign cores (Next Seq )

  Dataset EGAD00001004466

• Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance through Serial Biopsies and Comprehensive Analytics

  This dataset accompanies the manuscript "Characterizing Advanced Breast Cancer Heterogeneity and Treatment Resistance Through Serial Biopsies and Comprehensive Analytics". In this study we discuss the clinical implications and consequences of molecular heterogeneity in a metastatic breast cancer patient identified through serial biopsies, multi-omic analysis, and longitudinal patient monitoring, performed through the precision oncology program, Serial Measurements of Molecular and Architectural Responses to Therapy (SMMART). This case study describes a metastatic breast cancer patient with a highly heterogenous tumor that began as ER positive/HER2 negative on diagnosis, then was found to be ER negative/HER2 positive in a metastatic liver lesion (Study biopsy #1), and finally triple negative (TNBC) in another metastatic liver biopsy (Study biopsy #2). Multi-omic assays included clinical genomic panel sequencing, clinical immunohistochemistry, a clinical protein profiling assay (Intracellular Signaling Protein Profiling), RNA sequencing, and RPPA. Here we provide the expression data from RNAseq and RPPA data on Study biopsy #2 for the patient discussed in this case report. Note: The first liver biopsy did not yield sufficient material for RNAseq or RPPA.Raw RNA FASTQ files from Study biopsy #2 of the liver Gene expression (RNAseq) files from Study biopsy #2Protein expression (RPPA) files (level 2, 3, 4) for Study biopsy #2

  Study phs002321

• RNAseq data of metastatic breast cancer samples

  Paired-end RNA sequencing data from 28 metastatic breast cancer samples. Sequencing was performed on the Illumina NovSeq 6000 and HiSeq 4000 using TruSeq Stranded mRNA Kit.

  Dataset EGAD50000000818

• PAMELA - 142 patients with HER2+ breast cancer

  RNA sequencing data of 141 samples from 141 patients with HER2+ breast cancer treated withletrozole or tamoxifen (SOLTI-1114 PAMELA trial)

  Dataset EGAD00001009054

• scRNA dataset for 15 samples

  This dataset includes single cell (sc)RNA-seq of N=3 patient-derived xenograft (PDX) samples profiled at baseline (no drug treatment) and N=12 samples from one PDX profiled during the course of treatment (N=1 sample each for 4 treatment arms x 3 timepoints). The dataset includes bam files that have been purged of reads mapping to cells where a majority of the reads from that cell mapped to the mouse genome. All scRNA-seq data were generated using the Chromium Next GEM Single Cell 5' Reagent Kits v2 (Dual Index) from 10X Genomics.

  Dataset EGAD50000001424

• Single-nuclei multiomic analysis of KOLF2.1J human induced pluripotent stem cells-derived differentiated dopaminergic neurons

  Datasets consists of 12 samples of nuclei from hIPSCs differentiated optimized protocols for generating dopaminergic neurons. 6 single nuclei (RNA-ATAC) samples of XMAS protocol with cells collected at days 0,11,16,28,42 and 56 after differentiation. 3 snRNA-seq samples from AMS protocol of cells collected at days 14,16 and 28 after differentiation and 3 snRNA-seq samples with AMS protocol until day 14 grafted and grafts processed after 5 months grafting.

  Dataset EGAD50000002054

• Single-cell RNA sequencing dissects gene-environment interactions on gene expression and regulation in immune cells.

  This study aims to dissect gene-environment interactions on gene expression and regulation in immune cells using scRNA-seq data from 120 donors being stimulated with 3 pathogens, for 2 different timepoints. More specifically, we identified context- and cell-type-specific eQTLs and co-expression QTLs in PBMCs.

  Study EGAS00001005376

• T cell transcriptional gradient

  There are two datasets: 1. scRNA-seq of human cutaneous immune cells from psoriasis patients. These include pre- and post-Tildrakizumab treated patients and come in a BAM file format. 19006FL-25-01 19006FL-38-01 19006FL-32-01-03 19006FL-33-01 19006FL-28-01-05 19006FL-35-01-01 2. RNA-seq of ZFP36L2 CRISPIR deleted Human T cells are FASTQ files. 19006XR-30-05 19006XR-30-04 19006XR-30-02 19006XR-30-01 19006XR-26-05 19006XR-26-04 19006XR-26-02 19006XR-26-01 19006R-22-04 19006R-22-08 19006R-22-05 19006R-22-01

  Dataset EGAD00001009677

• bulk RNAseq analysis of tumor from 3 non-muscle-invasive bladder cancer patients

  We performed screenings of patient-specific neo-epitopes by tumor-infiltrating CD8+ T lymphocytes of 4 non-muscle invasive bladder cancer patients. After the tumor resection, these patients went on to receive BCG treatment as they had high-risk disease. In order to select the neo-epitopes, we performed bulk RNAseq of the bladder tumor to select the mutated genes that were expressed in the tumor (TPM >1). For patient UC2, 3 different tumors were resected at the same time and RNA from these 3 tumors were sent separately for RNAseq. For patient UC4, no RNA was available, so this patient is not included in this study.

  Study EGAS50000001383

• Clonal Evolution and Heterogeneity of Osimertinib Acquired Resistance Mechanisms in EGFR Mutant Lung Cancer

  Current understanding of acquired resistance to osimertinib in EGFR-mutant lung cancer is largely based on the use of targeted sequencing platforms and cell-free DNA. In this study we examined the clonal evolution and heterogeneity of acquired resistance mechanisms to osimertinib through whole exome and RNA-sequencing of prospectively collected pre-and post-osimertinib resistant tumors and metastases at rapid autopsy. We enrolled 34 patients with histologically confirmed EGFR mutant advanced lung adenocarcinoma in a prospective Phase II clinical trial to evaluate osimertinib treatment and the use of local ablative therapy (LAT) for oligoprogressive disease (NCT02759835) in EGFR mutant NSCLC from April 2016 until data cutoff in September 2019. Patients were not selected based on oligometastatic status, rather 32/34 patients had multiple organ involvement, including lung, liver, adrenal, brain and bone. Biopsies or surgeries were performed pre-osimertinib treatment and at first progression on osimertinib. A subset of patients also underwent biopsies at second progression on osimertinib, and following end of life in-patient hospice and rapid autopsy upon death. 63% (n=15/24) of first-line osimertinib and 50% (n=5/10) of second-line osimertinib treated patients had RECIST defined partial response. Overall, 21 patients had RECIST defined first progression on osimertinib. Two patients on this trial discontinued treatment and were lost to follow up. Of the 21 patients who had RECIST defined progression, 14 patients underwent LAT (8 first-line and 6 second-line). Twelve patients had paired pre- and post-osimertinib resistant tumors and three had post-osimertinib resistant tumors available for analysis. Four patients (LAT002, LAT006, LAT014 and LAT021) consented for a rapid autopsy protocol and underwent rapid autopsy after deatH. To elucidate acquired osimertinib resistance mechanisms, we performed whole-exome (WES), targeted sequencing and RNA-seq on 66 tumors and matched germline DNA from tumor tissue obtained from a total of 15 patients.

  Study phs002001

• Pseudogene RNAseq

  RNA sequencing to validate findings of somatic pseudogenes acquired during cancer development

  Dataset EGAD00001000732