Similar Datasets
Project description:Prospective study of accuracy of colonic polyp characterisation in vivo using high resolution white light endoscopy, narrow band imaging and chromoendoscopy.
| 2071358 | ecrin-mdr-crc
Project description:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2022-11-09 | GSE210651 | GEO
Project description:The identification and characterisation of Circulating Tumour Cells (CTCs) is important to get insights into the biology of metastatic cancers, monitoring disease progression, and potential use in liquid biopsy-based personalised cancer treatment. The major limitation of CTC isolation is due to their heterogeneous nature, altered phenotype from primary tumour and availability in limited numbers. In the past years, several techniques have been developed to detect CTCs from peripheral blood, based on canonical markers. These methods are, however, prone to miss a larger set of CTCs due to variable or no expression of these markers. Furthermore, CTC enrichment processes are not free from White Blood Cell (WBC) contamination. Single cell RNA sequencing (scRNA-Seq) of CTCs provides a wealth of information about their tumors of origin as well as their fate. The first and most important roadblock encountered in CTC scRNA-Seq data analysis is confirmation of CTC capture. We present unCTC, an R software for unsupervised and semi-supervised characterisation of CTC transcriptomes, in contrast with WBCs. unCTC features various standard and novel computational/statistical modules for clustering, Copy Number Variation (CNV) inference, and marker based verification of the malignant phenotypes. Notably, we propose Deep Dictionary Learning using K-means clustering cost (DDLK) that robustly clusters scRNA-Seq profiles of CTCs and WBC contaminates to characterise heterogeneity among the concerned cell population. Interestingly, DDLK performs better as gene expression data is transformed into pathway enrichment profiles. To demonstrate the utility of unCTC, we produce scRNA-Seq profiles of breast CTCs enriched by the integrated ClearCell® FX/PolarisTM workflow that works on the principles of size selection and negative enrichment for CD45, a pan leukocyte marker.
2022-11-09 | GSE186288 | GEO
Project description:Characterisation of human PSC-derived cardiovascular cell types
| PRJNA608877 | ENA
Project description:Vascularization and maturation options for cardiac tissue engineered structures are currently intensively investigated. Therefore, the generation and characterisation of all cardiovascular cell types from human pluripotent stem cells (hPSC; either induced -iPSC- or embryonic -hESC) are of particular interest. In our group, differentiation and selection methods were described for obtaining highly pure hPSC-derived cardiomyocytes (CM; selected for αMHC), endothelial cells (EC; selected for CD31) and PDGFRβ expressing cardiac pericyte-like cells (PC). With the purpose of identifying cell type-related mechanisms in co-culture and tissue formation, gene expression profile of hPSC-derived CM, ECs, and PCs was compared to their undifferentiated progeny (hPSC) as well as to primary pericytes (hPC-PL) and fibroblasts (HFF).
2020-11-30 | GSE145957 | GEO
Project description:Here we aim to demonstrate a novel approach enabling standardised and rapid characterisation of transplanted xenografts in the rodent brain. The approach employs bulk tissue dissection, inclusive of the grafted human cells and surrounding host (rodent) tissue, and utilises differences in the RNA sequences between the species to discriminate the xenograft from host gene expression. To demonstrate and validate this technique, we assessed grafts of undifferentiated human stem cells, immature neural progenitors and mature neurons following transplantation into the rodent brain. Mixed species RNAseq was conducted on the bulk dissected tissue, and a high stringency analysis pipeline developed to discriminate the human reads. Xenograft-specific RNAseq allowed accurate profiling the complete transcriptome of the transplant with high sensitivity and accuracy. The profile allowed the identification of novel gene expression and an unbiased characterisation of graft composition, providing a valuable tool for the analysis of xenografts. This characterisation will be especially crucial for the characterisation of grafts in pre-clinical and batch testing of therapeutic cell preparations prior to translation to the clinic.
2019-08-31 | GSE126804 | GEO
Project description:Tracheoesophageal fistula characterisation
| PRJNA623592 | ENA
Project description:Background: The differential expression pattern of microRNAs (miRNAs) during mammary gland development might provide insights into their role in regulating the homeostasis of the breast epithelium. Our aim was to analyse these regulatory functions by deriving a comprehensive tissue-specific combined miRNA and mRNA expression profile of post-natal mouse mammary gland development. We measured the expression of 318 individual murine miRNAs by bead-based flow-cytometric profiling of whole mouse mammary glands throughout a 16-point developmental time course, including juvenile, puberty, mature virgin, gestation, lactation, and involution stages. In parallel whole-genome mRNA expression data were obtained. Results: One third (n = 102) of all murine miRNAs analysed were present during mammary gland development. MicroRNAs were represented in seven temporally co-expressed clusters, which were enriched for both miRNAs belonging to the same family and breast cancer-associated miRNAs. Global miRNA and mRNA expression was significantly reduced during lactation and the early stages of involution after weaning. For most detected miRNA families we did not observe systematic changes in the expression of predicted targets. For miRNA families whose targets did show significant changes, we observed inverse patterns of miRNA and target expression. The datasets are made publicly available and the combined expression profiles represent an important community resource for mammary gland biology research. Conclusions: MicroRNAs were expressed in co-regulated clusters during mammary gland development. Breast cancer-associated miRNAs were significantly enriched in these clusters. The mechanism and functional consequences of this miRNA co-regulation and its correlation with mRNA expression provide new avenues for research into mammary gland biology and generates candidates for functional validation. This SuperSeries is composed of the following subset Series: GSE15054: Characterisation of microRNA expression in post-natal mouse mammary gland development [gene] GSE15055: Characterisation of microRNA expression in post-natal mouse mammary gland development [miRNA] Refer to individual Series
2010-06-25 | E-GEOD-15057 | biostudies-arrayexpress