Decoding breast cancer tissue-stroma interactions using species-specific sequencing
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA277852 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR1909090.fastq.gz | Fastqsanger.gz | |||
| SRR1909100.fastq.gz | Fastqsanger.gz | |||
| SRR1909091.fastq.gz | Fastqsanger.gz | |||
| SRR1909101.fastq.gz | Fastqsanger.gz | |||
| SRR1909092.fastq.gz | Fastqsanger.gz |
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Project description:Decoding genome-wide effects of experimental tissue-tissue or cell-cell interactions is important, for example, to better understand tumor-stroma interactions after transplantation (xenografting). Transcriptome analysis of intermixed human and mouse cells has frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression. To circumvent this problem, we perform a bioinformatics-based genome-wide transcriptome analysis technique separating the mouse and human transcriptome part of a dataset, which allows a mixed mouse and human cell population to be sequenced without prior cell sorting. We use the new technology – which we call S3 (“S-cube” for Species-specific sequencing) technology – to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of luminal (MCF7) and basal-type (MDA-MB-231) human breast cancer cells into mammary fat pad tissue in mice. Analysis of Notch response to ligand in three settings: co-culture, immobilized ligand and xenografted tumors
2015-08-12 | E-GEOD-66744 | biostudies-arrayexpress
Project description:Decoding genome-wide effects of experimental tissue-tissue or cell-cell interactions is important, for example, to better understand tumor-stroma interactions after transplantation (xenografting). Transcriptome analysis of intermixed human and mouse cells has frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression. To circumvent this problem, we perform a bioinformatics-based genome-wide transcriptome analysis technique separating the mouse and human transcriptome part of a dataset, which allows a mixed mouse and human cell population to be sequenced without prior cell sorting. We use the new technology – which we call S3 (“S-cube” for Species-specific sequencing) technology – to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of luminal (MCF7) and basal-type (MDA-MB-231) human breast cancer cells into mammary fat pad tissue in mice.
2015-08-12 | GSE66744 | GEO
Project description:IntroductionDecoding transcriptional effects of experimental tissue-tissue or cell-cell interactions is important; for example, to better understand tumor-stroma interactions after transplantation of human cells into mouse (xenografting). Transcriptome analysis of intermixed human and mouse cells has, however, frequently relied on the need to separate the two cell populations prior to transcriptome analysis, which introduces confounding effects on gene expression.MethodsTo circumvent this problem, we here describe a bioinformatics-based, genome-wide transcriptome analysis technique, which allows the human and mouse transcriptomes to be decoded from a mixed mouse and human cell population. The technique is based on a bioinformatic separation of the mouse and human transcriptomes from the initial mixed-species transcriptome resulting from sequencing an excised tumor/stroma specimen without prior cell sorting.ResultsUnder stringent separation criteria, i.e., with a read misassignment frequency of 0.2 %, we show that 99 % of the genes can successfully be assigned to be of mouse or human origin, both in silico, in cultured cells and in vivo. We use a new species-specific sequencing technology-referred to as S(3) ("S-cube")-to provide new insights into the Notch downstream response following Notch ligand-stimulation and to explore transcriptional changes following transplantation of two different breast cancer cell lines (luminal MCF7 and basal-type MDA-MB-231) into mammary fat pad tissue in mice of different immunological status. We find that MCF7 and MDA-MB-231 respond differently to fat pad xenografting and the stromal response to transplantation of MCF7 and MDA-MB-231 cells was also distinct.ConclusionsIn conclusion, the data show that the S(3) technology allows for faithful recording of transcriptomic changes when human and mouse cells are intermixed and that it can be applied to address a broad spectrum of research questions.
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Project description:Retrieving high-resolution chromatin interactions and decoding enhancer regulatory potential in silico
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Project description:The advent of the chromosome conformation capture (3C) and related technologies has profoundly renewed our understaning of three-dimensional chromatin folding in mammalian nuclei. Alongside these experimental approaches, numerous computational tools for handling, normalizing, visualizing, and ultimately detecting interactions in 3C-type datasets are being developed. Here, we present Bloom, a comprehensive method for the analysis of 3C-type data matrices on the basis of Dirichlet process mixture models that addresses two important open issues. First, it retrieves occult interaction patterns from sparse data, like those derived from single-cell or “native” Hi-C experiments; thus, bloomed sparse data can now be used to study interaction landscapes at high-resolution. Second, it detects enhancer-promoter interactions with high sensitivity and inherently assigns an interaction frequency score (IFS) to each contact. Using enhancer perturbation data of different throughput, we show that IFS accurately quantifies the regulatory influence of each enhancer on its target promoter. As a result, Bloom allows decoding of complex regulatory landscapes by generating functionally-relevant enhancer atlases solely on the basis of 3C-type data.
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Project description:Tumor-associated breast stroma was laser-capture microdissected from IDC breast cancer cases. The goal of the study was to characterize the heterogeneity of breast tumor-assocaited stroma and identify gene expression signatures predictive of clinical outcome. Keywords: disease state analysis
2008-04-11 | GSE9014 | GEO
Project description:Breast cancers display a high degree of diversity between and within tumors due to variations in both tumor cells and non-malignant cells of the tumor microenvironment (TME). While most studies on the TME have focused on stroma located within the tumor mass, few studies have examined the peri-tumoral microenvironment, which extends beyond the tumor margins and includes the surrounding, benign-appearing tissues. Here, we examined the heterogeneity in tumors and peri-tumoral stroma from 10 ER+PR+HER2- invasive breast carcinomas, through multi-region transcriptomic profiling. Tumor samples were obtained from the center, superior, inferior, medial, and lateral (C, S, Inf, M, L) regions of each tumor. Peritumoral samples were obtained from four radial directions corresponding to the regions the tumor samples were collected from (S, Inf, M, L) and from three zones of increasing distances from the tumor margins (Zone 1: 1-3 cm, Zone 2: 3-5 cm, Zone 3: 5-7 cm). To represent the normal breast transcriptomic state, 5 non-directional samples were each obtained from 3 non-tumor bearing breasts (NTB) - 1 patient had undergone cosmetic reduction mammoplasty and 2 patients were germline BRCA2-mutation carriers who had undergone risk-reducing mammoplasties. Total RNA was isolated and microarray was performed using the human Clariom™ S Assay (ThermoFisher Scientific). In total, 144 transcriptomic profiles (47 Tumor, 82 peri-tumoral stroma, 15 NTB) were analyzed. Heterogeneity was observed both within and between the tumor samples, highlighting that unlike the histological classification, an individual tumor is often comprised of several molecular sub-types. Similarly, the peri-tumoral stroma was also heterogeneous, albeit independent of the tumor heterogeneity. Four distinct stromal clusters were noted, which differed in their molecular pathways and cell type composition. Among these, the adipose-enriched stroma that had inflammatory cancer-associated fibroblasts and innate immune cells was significantly associated with poorer overall survival, in contrast to the myofibroblast-enriched stroma. These data together suggest that the peri-tumoral heterogeneity may be an important determinant of the evolution and treatment of breast cancers.
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