UnCTC: Unbiased characterisation of single circulating tumor cell transcriptomes [scRNA-seq]
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA773126 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR16507400_1.fastq.gz | Fastqsanger.gz | |||
| SRR16507400_2.fastq.gz | Fastqsanger.gz | |||
| SRR16507401_1.fastq.gz | Fastqsanger.gz | |||
| SRR16507401_2.fastq.gz | Fastqsanger.gz | |||
| SRR16507402_1.fastq.gz | Fastqsanger.gz |
Items per page: 5 1 - 5 of 162 |
Similar Datasets
Project description:unCTC: Unbiased characterisation of single circulating tumor cell transcriptomes
| PRJNA751074 | ENA
Project description:unCTC: Unbiased characterisation of single circulating tumor cell transcriptomes II
| PRJNA866550 | 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: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:RNA-protein interactions determine the cellular fate of RNA and are central to regulating gene expression outcomes in health and disease. To date, no method exists to identify the proteins bound to specific regions in endogenous RNAs in an unbiased fashion. Here, we develop SHIFTR (Selective RNase-H-mediated interactome framing for target RNA regions), an efficient and scalable approach to identify proteins bound to selected RNA regions in live cells. Compared to state-of-the-art RNA antisense purification techniques, SHIFTR is superior in accuracy, captures close to zero background interactions and requires orders of magnitude lower input material. We establish SHIFTR workflows for targeting RNA biotypes of different length and abundance, including short and long non-coding RNAs, as well as mRNAs and demonstrate that SHIFTR is compatible with multiplexed RNA interactome release in a single experiment. Using SHIFTR, we comprehensively identify interactions of cis-regulatory elements located at the 5ʹ and 3ʹ-terminal regions of the authentic SARS-CoV-2 RNA genome in infected cells and accurately recover known and novel interactions linked to the function of these viral RNA elements. SHIFTR enables the systematic mapping of region-resolved RNA interactomes for any RNA in any cell type, which has the potential to revolutionize our understanding of the transcriptomes of pathogens and their hosts.
2024-01-18 | PXD044722 | Pride
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:Anopheline mosquitoes transmit Plasmodium parasites to humans, and are responsible for an estimated 219 million cases of malaria, leading to over 400,000 deaths annually. The mosquito’s immune system limits Plasmodium infection in several ways, and hemocytes, the insect white blood cells, are key players in these defense responses. However, the full functional diversity of mosquito hemocytes and their developmental trajectories have not been established. We use single cell RNA sequencing (scRNA-seq) to analyze the transcriptional profiles of individual mosquito hemocytes in response to blood feeding or infection with Plasmodium. Circulating hemocytes were collected from adult A. gambiae M form (A. coluzzii) females that were either kept on a sugar meal or fed on a healthy or a Plasmodium berghei-infected mouse. Transcriptomes from 5,383 cells (collected 1, 3, and 7 days after feeding) revealed nine major cell clusters.
2020-08-27 | E-MTAB-9240 | biostudies-arrayexpress
Project description:We studied the membrane protein compositions of Streptococcus pneumoniae WT and scRNA mutant strains, with or without the CSP1 induction into competence state.
2024-08-19 | PXD054463 | Pride
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:Characterisation of human CD4+ cells to gain more insights into regulatory processes.
2023-03-30 | GSE220530 | GEO