145e4b84-f490-4618-b12a-85f5cfc9bccc - samples
Ontology highlight
ABSTRACT:
PROVIDER: EGAD00001010130 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:We developed a novel antibody-drug conjugate (ADC), C6-EBET. To analyze mechanism of action of C6-EBET, we obtained bulk and single cell RNA-seq data of two patient-derived xenograft (PDX) models derived from pancreatic ductal adenocarcinoma (PDAC) patients. The two PDX models were treated with gemcitabine, C6-EBET or a non-targeting control ADC (HEL3-EBET), and then gene expression changes were analyzed with bulk RNA-seq. In addition, we characterized stromal cells of the two PDX models with scRNA-seq.
| EGAS00001007070 | EGA
Project description:We report the application of single-molecule-based sequencing technology for high-throughput profiling of genes in E-, M-BCSCs and bulk tumor cells in two PDX models of triple negative breast cancer .
2018-06-05 | GSE115302 | GEO
Project description:Long non-coding RNAs (lncRNAs) comprise a diverse class of transcripts that can regulate molecular and cellular processes in brain development and diseasee. LncRNAs exhibit cell type- and tissue-specific expression, but little is known about the expression and function of lncRNAs in the developing human brain. Here, we deeply profiled lncRNAs from polyadenylated and total RNA obtained from human neocortex at different stages of development and integrated this resource to analyze the transcriptomes of single cells. While lncRNAs were generally detected at low levels in whole tissues, single cell transcriptomics revealed that many lncRNAs are abundantly expressed in individual cells and are cell type-specific. Furthermore, we used CRISRPi to show that LOC646329, a lncRNA enriched in radial glia but detected at low abundance in tissues, regulates cell proliferation. The discrete and abundant expression of lncRNAs among individual cells has important implications for both their biological function and utility for distinguishing neural cell types. 16 Bulk Tissue Samples from GW13-23; 226 Single Cells from GW19.5-23.5 ------------------ bulk_tpm.polya.txt: bulk RNA-seq expression; using polyA full reference scell_ncounts.genes.thresh.txt: single cell RNA-seq expression; using polyA stringent reference; includes 50 GW16, GW21, GW21p3 cells previously analyzed (Pollen et. al. 2014) polya_RNA_stringent_ref.gtf: bulk RNA-seq polyA stringent transcriptome reference polya_RNA_full_ref.gtf: bulk RNA-seq polyA full transcriptome reference total_RNA_stringent_ref.gtf: bulk RNA-seq total stringent transcriptome reference total_RNA_full_ref.gtf: bulk RNA-seq total full transcriptome reference GW13_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW13_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW14.5_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW16_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW21_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_1_total_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_polya_plus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_minus.bw: strand-specific bulk RNA-seq alignment signal GW23_2_total_plus.bw: strand-specific bulk RNA-seq alignment signal
2016-03-28 | E-GEOD-71315 | biostudies-arrayexpress
Project description:Bulk RNA-sequencing of astrocytes in the APP NL-F and APP PS1 models of ß-amyloidopathy, in which aspects of AD-related pathology progress at different speed, shows age-dependent gene expression changes. However, bulk RNA-seq does not provide insight into the heterogeneity of expression within this cell type, particularly relevant for such models, where reactive astrogliosis is most prominent in the vicinity of plaques. To investigate astrocyte heterogeneity in ß-amyloidopathy models, we thus performed single cell RNA-sequencing on astrocytes separated by FACS.
2024-03-21 | E-MTAB-13937 | biostudies-arrayexpress
Project description:This ADPKD project has 3 different experiments, 7 different single cell RNA-seq data, 5 different single nuclei RNA-seq data and 6 different bulk ATAC-seq data objects.
| EGAD00001009328 | EGA
Project description:RNA-seq of SCLC PDX models treated with EP
| PRJNA434676 | ENA
Project description:Purpose: The goal of this study is to establish and molecularly characterize non-small cell lung cancer (NSCLC) organoids. Generation of NSCLC organoids would provide additional preclinical models for drug screening and biomarker discovery. Methods: Patient lung tumors and previously established patient-derived xenografts (PDX) were processed to generate organoids. Total RNA was extracted and subjected to RNA-seq to examine the gene expression similarity between patient/PDX/organoid of the same model using t-SNE clustering. Results: Through RNA-sequencing, we generated TPM values of patient, PDX and organoid samples of 5 models. t-SNE analysis showed that the patient/PDX/organoid of the same models clustered together. The lung adenocarcinoma samples formed a separate cluster from the squamous cell carcinoma samples based on gene expression. Conclusions: Our study introduces the establishment of NSCLC organoids and demonstrates the gene expession similarity of the organoid model to its corresponding PDX and patient sample.
2019-11-26 | GSE119004 | GEO
Project description:Treatment paradigms for patients with upper tract urothelial carcinoma (UTUC) are typically extrapolated from studies of bladder cancer despite their distinct clinical and molecular characteristics. A major hurdle to the advancement of UTUC research is the lack of disease-specific models. Here, we report the establishment of patient derived xenograft (PDX) and cell lines models that reflect the heterogeneity of the human disease. Models demonstrated high genomic concordance with the tumors from which they were derived with muscle-invasive tumors more likely to successfully engraft. Treatment of PDX with chemotherapy recapitulated responses observed in the patients. Analysis of a S310F HER2 mutant PDX suggested that an antibody drug conjugate targeting HER2 would have superior efficacy to HER2-selective kinase inhibitors. In sum, the biologic and phenotypic concordance between patient and PDXs suggests that these models could facilitate studies of intrinsic and acquired resistance and the development of personalized medicine strategies for UTUC patients.
2020-01-29 | GSE134292 | GEO
Project description:Bulk RNA-seq for cALL patient-derived PDX samples
| EGAD00001006133 | EGA