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:Characterisation of ImLECs
| PRJEB97700 | ENA
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