UnCTC: Unbiased characterisation of single circulating tumor cell transcriptomes
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA751074 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15307500_1.fastq.gz | Fastqsanger.gz | |||
| SRR15307500_2.fastq.gz | Fastqsanger.gz | |||
| SRR15307501_1.fastq.gz | Fastqsanger.gz | |||
| SRR15307501_2.fastq.gz | Fastqsanger.gz | |||
| SRR15307502_1.fastq.gz | Fastqsanger.gz |
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Project description:unCTC: Unbiased characterisation of single circulating tumor cell transcriptomes II
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Project description:unCTC: Unbiased characterisation of single circulating tumor cell transcriptomes [scRNA-seq]
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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:Metastatic colorectal cancer (mCRC) relies on the detachment of aggressive malignant cells from the primary tumor into the bloodstream, these Circulating Tumor Cells (CTC) being the principal source of the further metastasis. Here, we approached the massive molecular profiling of the CTC population isolated from mCRC patients. Clinically, the presence of CTC is associated with poor prognosis and there exists a clear necessity for more specific and efficient chemotherapies in the treatment of mCRC. Immunoisolation of CTC from these patients combined with a whole transcriptome amplification and hybridization onto gene expression arrays, lead us to specifically describe for the first time a CTC population with adhesive and migratory characteristics, in addition to a modulated expression of genes related to cell death and survival. Furthermore, bioinformatic analysis provided with an armamentarium of highly specific and sensitive valuable markers that should impact on the management and follow-up of mCRC patients. Finally, molecular profiling of CTC resulted in the identification of new therapeutic targets, like TGF-β1 impairing, specifically targeting the CTC population which should improve the efficacy in the eradication and prevention of CRC metastasis. In conclusion, molecular profiling of CTC represents an innovative and promising approach in the clinical management of mCRC patients. Circulating Tumor Cells (CTC) were immunoisolated from six metastatic colorectal patients (mCRC). Moreover, blood from three healthy donors were obtained, immunoisolating non-specific cellularity background. RNA was extracted and globally amplified with a Whole Transcriptome Amplification system. Samples were hybridized onto agilent arrays and CTC specific genes were obtained. Gene networks were built with IPA software and a gene ontology analysis was performed. Eleven candidate genes were validated by real time PCR. Finally, TGFB1 was evaluated as a potential drugable target against CTC population.
2012-06-29 | E-GEOD-31023 | biostudies-arrayexpress
Project description:Circulating Tumor Cells (CTCs) are shed from primary tumors into the bloodstream, mediating the hematogenous spread of cancer to distant organs. Using a pancreatic cancer mouse model, we applied a microfluidic device to isolate CTCs independently of tumor epitopes, subjecting these to single cell RNA-sequencing. This study was conducted to determine the heterogeneity of pancreatic CTCs and to compare these CTCs to matched primary tumors, cell line controls (NB508 cancer cell line and MEF non-cancer cell line), primary tumor single cells, and normal leukocytes/WBCs. We profiled RNA from 75 single cells circulating in mouse blood enriched for circulating tumor cells from 5 mice, 12 single cells from a mouse embryonic fibroblast cell line, 16 single cells from the nb508 mouse pancreatic cancer cell line, 12 single mouse white blood cells, 18 single GFP lineage-traced circulating tumor cells from two mice, 20 single GFP lineage-traced cancer cells from the primary pancreatic tumor of a mouse, and 34 dilutions to 10 or 100 picograms of total RNA from mouse primary pancreatic tumors from 4 mice.
2014-09-22 | E-GEOD-51372 | biostudies-arrayexpress
Project description:Metastatic colorectal cancer (mCRC) relies on the detachment of aggressive malignant cells from the primary tumor into the bloodstream, these Circulating Tumor Cells (CTC) being the principal source of the further metastasis. Here, we approached the massive molecular profiling of the CTC population isolated from mCRC patients. Clinically, the presence of CTC is associated with poor prognosis and there exists a clear necessity for more specific and efficient chemotherapies in the treatment of mCRC. Immunoisolation of CTC from these patients combined with a whole transcriptome amplification and hybridization onto gene expression arrays, lead us to specifically describe for the first time a CTC population with adhesive and migratory characteristics, in addition to a modulated expression of genes related to cell death and survival. Furthermore, bioinformatic analysis provided with an armamentarium of highly specific and sensitive valuable markers that should impact on the management and follow-up of mCRC patients. Finally, molecular profiling of CTC resulted in the identification of new therapeutic targets, like TGF-β1 impairing, specifically targeting the CTC population which should improve the efficacy in the eradication and prevention of CRC metastasis. In conclusion, molecular profiling of CTC represents an innovative and promising approach in the clinical management of mCRC patients.
2012-06-30 | GSE31023 | GEO
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