Analysis of cell type heterogeneity in breast tissue.
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA888140 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Analysis of cell type heterogeneity in breast tissue
| PRJNA888205 | ENA
Project description:Analysis of cell type heterogeneity in breast tissue II
| PRJNA940123 | ENA
Project description:We used PIP-seq single cell RNA-sequencing to study single cells from dissociated breast tissue
2023-01-12 | GSE202913 | GEO
Project description:We used PIP-seq single cell RNA-sequencing to study single cells from dissociated breast tissue
2023-01-12 | GSE215060 | GEO
Project description:We used 10X Chromium single cell RNA-sequencing to study single cells from dissociated breast tissue
2023-03-02 | GSE226391 | GEO
Project description:Tissue neutrophil heterogeneity
| PRJNA594618 | ENA
Project description:We aimed to assess breast tissue methylation in breast cancers (triple negative breast cancer) as well as control breast tissue and breast tissue from women with increased breast cancer risk. The dataset includes methylation data from triple negative breast cancer tissue, breast tissue from risk-reducing surgeries (BRCA1/2 mutation carriers), normal breast tissue (cosmetic surgeries) as well as normal tissue adjacent to breast cancers. This dataset also comprises methylation data from controls or BRCA1/2 mutation carriers before and after mifepristone or placebo treatment.
| EGAS00001005070 | EGA
Project description:The use of single cell transcriptomics provides previously inaccessible insights into cellular heterogeneity and lineage dynamics of the mammary gland allowing for a better understanding of normal mammary gland function as well as breast cancer initiation and progression. Especially for human mammary gland research, limited tissue accessibility and restriction to ex vivo techniques reinforce the importance of reliable cross-study comparison of single-cell transcriptomic data. However, it is unclear to what extent differences in breast tissue dissociation influence composition and transcriptomic profiles of isolated cells. Here, we used single-cell RNA sequencing to compare human mammary cell populations isolated from a single mammoplasty patient by varying enzymatic dissociation protocols differing in duration (3 or 16 hours) and agitation speed (10 rpm or 100 rpm). Protocol A (3 hours, 100 rpm), protocol B (16 hours, 100 rpm) and protocol C (16 hours, 10 rpm) were used to extract cell fragments from tissue which were either frozen down directly or further dissociated into single cells prior to cryopreservation. Samples were then prepared together for 10x scRNA-sequencing, where the fragments were defrosted and freshly dissociated and were loaded together with the defrosted single cells. From each of the protocols we sequenced similar numbers of cells isolated from fragments (Protocol A: 3,586 cells, Protocol B: 2,809 cells and Protocol C: 4,796 cells), finding an average of 7,427 unique molecular identifiers and 2,445 genes detected per cell. Overall, we detected a greater abundance and heterogeneity of stromal cell types, such as fibroblasts and endothelial cells at a lower agitation speed. Moreover, an extended duration of tissue dissociation governed an overall cellular oxidative stress response together with a downregulation of breast cancer associated genes and a cell-type specific downregulation of lineage markers. Thus, our systematic analysis of dissociation-induced compositional and transcriptional bias in human breast tissue samples yields useful information to avoid misinterpretation of cellular heterogeneity and lineage composition.
2023-06-30 | E-MTAB-9844 | biostudies-arrayexpress
Project description:Tumor-associated breast vasculature was laser-cappture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-associated vasculature and identify gene expression signatures predictive of clinical outcome. common reference design, 32 samples
2010-09-19 | E-GEOD-15363 | biostudies-arrayexpress
Project description:The heterogeneity and the complex cellular architecture have a crucial effect on breast cancer progression and response to treatment. However, deciphering the neoplastic subtypes and their spatial organization is still challenging. Here we combine single-nucleus RNA sequencing (snRNA-seq) with a microarray-based spatial transcriptomics (ST) to identify cell populations and their spatial distribution in breast cancer tissues. Malignant cells are clustered into distinct sub-populations. These cell clusters not only have diverse features, origins and functions, but also emerge to the crosstalk within subtypes. Furthermore, we find that these sub-clusters are mapped in distinct tissue regions, where discrepant enrichment of stromal cell types are observed. We also inferred the abundance of these tumorous subpopulations by deconvolution of large breast cancer RNA-seq cohorts, revealing differential association with patient survival and therapeutic response. Our study provides a novel insight for the cellular architecture of breast cancer and potential therapeutic strategies.
2022-03-19 | GSE198745 | GEO