Browse
Submit Data
Databases
API
Help

Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

53 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

ScRNAseq of two medulloblastoma cell lines (ONS-76 and HDMB-03) grown in 3D hydrogel culture

ABSTRACT: Two medulloblastoma cell lines (ONS-76 and HDMB-03) were grown in 3D hyaluronan hydrogels for three weeks. We observed nodules forming showing different behavior and wanted to evaluate if these different nodules (slow vs fast vs non-growing, migrating and invading cells) are also characterised by different gene expression patterns. We performed this experiment on a SHH (ONS-76) and on a group 3 MB (HDMB-03) cell line to compare if certain subpopulations would be unique for the subgroups.

INSTRUMENT(S): NextSeq 500

ORGANISM(S): Homo sapiens

SUBMITTER: James Johnson

PROVIDER: E-MTAB-12591 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Exploiting evolutionary steering to induce collateral drug sensitivity in cancer

Project description:This study contains single cell RNA-seq of the non-small cell lung cancer line HCC827 in three conditions. Firstly we have sequenced the RNA of single cells of a culture of HCC827 cells grown in normal conditions (POT). In our study we then evolved two arms of the cell line, one in the presence of the drug gefitinib (40nM, G1) and the other in the presence of trametinib (100nM, T4) in HYPERflasks to avoid replating. These data were analysed using cellRanger using GRCh38. Output from cellRanger was filtered for a minimum number of detected genes and UMIs. Mitochondrial reads were excluded from analysis (Acar_2020_single_cell_raw_data.txt). The data was then imported and scaled using the package Seurat. Scaled data was then filtered for low expression of housekeeping genes. Additionally genes expressed in fewer than 20 cells were excluded from analysis. Data was then renormalised using linear normalisation and scaling on the filtered raw data (Acar_2020_single_cell_processed_data.txt). Principal component analysis was run on variable genes identified using Seurat and the top 44 component were used as input for t-SNE analysis. Clusters were then identified using FindClusters in Seurat (Acar_2020_single_cell_metadata.txt). For a full description see the associated publication.

2020-08-18 | E-MTAB-8809 | biostudies-arrayexpress

Single-cell Gene Expression Flex analysis on patient derived xenografts (PDX) of lung cancer cells with minimal host tumor microenvironment (TME) cells

Project description:To goal of the experiment is to assess whether we can profile enough cells corresponding to the host TME. In this PDX model, most of the cells correspond to the xenograft material, with limited amounts of host TME cells. To this aim we loaded 16 times more cells hybridized with the mouse probe set compared to the human hybridization.

2024-04-15 | E-MTAB-13968 | biostudies-arrayexpress

Single-cell Gene Expression Flex analysis on PC9 cells co-cultured with NIH-3T3 cells

Project description:The goal of the experiment is to determine whether it is feasible to use the 10x Genomics Gene Expression Flex solution in samples containing a mixture of human and mouse cells. First we tested how the presence of mouse cells impacted the data recovered by the use of the human whole trasnscriptome amplification (WTA) probe sets. Then we profiled a mixed species sample by using human and mouse probe sets in the same reaction. Finally, for samples with lower amounts of human cells we tried to correct the amount of cells recovered by the human and mouse probe sets by adjusting the amount of cells loaded from the respective hybridizations.

2024-04-15 | E-MTAB-13962 | biostudies-arrayexpress

Single-cell Gene Expression Flex analysis on cell line derived xenografts of PC9 cells treated with Osimertinib

Project description:The goal of the experiment is determine whether the mixing of human and mouse probe sets of the 10x Genomics Gene Expression Flex allows to profile cell line derived xenograft (CDX) samples. We profiled a CDX vehicle sample and a CDX sample after 10 days of treatment with Osimertinib at 25mg/kg.

2024-04-15 | E-MTAB-13963 | biostudies-arrayexpress

Multiple Early Factors Anticipate Post-Acute COVID-19 Sequelae

Project description:Post-acute sequelae of COVID-19 (PASC) represent an emerging global crisis. However, quantifiable risk-factors for PASC and their biological associations are poorly resolved. We executed a deep multi-omic, longitudinal investigation of 309 COVID-19 patients from initial diagnosis to convalescence (2-3 months later), integrated with clinical data, and patient-reported symptoms. We resolved four PASC-anticipating risk factors at the time of initial COVID-19 diagnosis: type 2 diabetes, SARS-CoV-2 RNAemia, Epstein-Barr virus viremia, and specific autoantibodies. In patients with gastrointestinal PASC, SARS-CoV-2-specific and CMV-specific CD8+ T cells exhibited unique dynamics during recovery from COVID-19. Analysis of symptom-associated immunological signatures revealed coordinated immunity polarization into four endotypes exhibiting divergent acute severity and PASC. We find that immunological associations between PASC factors diminish over time leading to distinct convalescent immune states. Detectability of most PASC factors at COVID-19 diagnosis emphasizes the importance of early disease measurements for understanding emergent chronic conditions and suggests PASC treatment strategies.

2022-02-01 | E-MTAB-10129 | biostudies-arrayexpress

Multi-omic analysis resolves coordinated immune responses to mild, moderate and severe COVID-19

Project description:This study utilizes multi-omic biological data to perform deep immunophenotyping on the major immune cell classes in COVID-19 patients. 10X Genomics Chromium Single Cell Kits were used with Biolegend TotalSeq-C human antibodies to gather single-cell transcriptomic, surface protein, and TCR/BCR sequence information from 254 COVID-19 blood draws (a draw near diagnosis (-BL) and a draw a few days later (-AC)) and 16 healthy donors.

2020-10-29 | E-MTAB-9357 | biostudies-arrayexpress

Single-cell RNA sequencing of self-maintaining lamina propria macrophages

Project description:Single-cell RNA sequencing of 697 YFP+ CD64+ lamina propria macrophages isolated from Cx3cr1CreERT2.Rosa26-LSL-YFP mice 35 weeks after tamoxifen administration

2018-07-30 | E-MTAB-6976 | biostudies-arrayexpress

Single cell sequencing of renal endothelial cell heterogeneity and metabolic transcriptome adaptation to dehydration.

Project description:Renal endothelial cells (RECs) from glomerular cortical and medullary kidney compartments are exposed to different microenvironmental conditions. Upon dehydration medullary RECs (mRECs) are exposed to extreme hyperosmolarity. However the heterogeneous phenotypes of RECs remain in-completely inventoried and how mRECs respond to dehydration is unknown. By single cell RNA-sequencing of >40000 RECs we identified 24 (including 8 novel) REC phenotypes highlighting extensive heterogeneity of RECs between and within the cortex glomeruli and medulla. In response to dehydration mRECs upregulated primarily the expression of genes involved in the hypoxia response glycolysis and surprisingly oxidative phosphorylation (OXPHOS). In vitro mRECs increased oxygen consumption in response to hyperosmolarity presumably to sustain ATP production for Na+/K+ ATPase pump-mediated salt excretion and to generate metabolic water during OXPHOS in order to counteract mREC hyperosmolarity unveiling a previously underappreciated role of OXPHOS. Overall RECs exhibit extensive heterogeneity and plasticity to adapt their metabolic transcriptome to overcome dehydration.

2019-12-12 | E-MTAB-8145 | biostudies-arrayexpress

Single cell RNAseq of 12-week old AKIMBA versus WT mouse retina

Project description:Single-cell RNA sequencing was performed on retinal tissue from 12-week-old wild-type and Akimba (Ins2AkitaxVEGF+/-) mice, which are known to replicate features of clinical diabetic retinopathy. The aim of this study was to provide deeper insight into the complex network of molecular and cellular changes that underlie diabetic retinopathy by measuring the transcriptional changes that occur in the different cellular compartments of the degenerating diabetic mouse retina. Retinas (n=4 for Akimba, n=2 for wild-type) were isolated in ice-cold Dulbecco’s Modified Eagle Medium. After rinsing with Dulbecco’s Phosphate-Buffered Saline containing 2% fetal bovine serum, each retina was incubated with 1mL digestion buffer (2mg/mL collagenase-P, 200U/mL DNAse-I (Sigma-Aldrich) in M199 medium (Life Technologies) at 37°C for 10min. Retinal tissue was further dissociated by trituration and the suspension was filtered through a 40µm cell strainer and centrifuged for 5min at 300xg (4°C). Pooled retinal single-cell suspensions from wild-type and Akimba were counted on a Luna-FL Cell Counter (Logos Biosystems) and libraries were prepared with the Chromium Single-cell 3’ V2 Chemistry Library Kit, Gel Bead & Multiplex Kit and Chip Kit (10X Genomics) aiming for 5000 cells per library. Barcoded libraries were sequenced on an Illumina HiSeq4000 in 25-8-98 paired-end configuration. The transcriptome data for 9474 retinal cells were analysed, yielding 15 clusters corresponding to eight distinct retinal cell types.

2020-05-23 | E-MTAB-9061 | biostudies-arrayexpress

Single Cell Analysis in trisomic tissues and organoids

Project description:Cortical organoids generated from mESC from a Down Syndrome (DS) mouse model (Ts65Dn) were dissociated for single cell sequencing to understand whether differential gene expression in DS brains affects cell diversity and how the molecular events that control the development of these diverse types of neocortical neurons are affected by the trisomy. These results were generated in parallel to a single cell experiment in forebrain fetal tissue from WT and Ts65Dn mice.

2023-03-15 | E-MTAB-12827 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data