UnCTC: Unbiased characterisation of single circulating tumor cell transcriptomes II
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA866550 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR20871600_1.fastq.gz | Fastqsanger.gz | |||
| SRR20871600_2.fastq.gz | Fastqsanger.gz | |||
| SRR20871601_1.fastq.gz | Fastqsanger.gz | |||
| SRR20871601_2.fastq.gz | Fastqsanger.gz | |||
| SRR20871602_1.fastq.gz | Fastqsanger.gz |
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Project description:unCTC: Unbiased characterisation of single circulating tumor cell transcriptomes
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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.
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Project description:Background. Pneumococcus is a major human pathogen and the polysaccharide capsule is considered its main virulence factor. Nevertheless, strains lacking a capsule, named non-typeable pneumococcus (NT), are maintained in nature and frequently colonise the human nasopharynx. Interest in these strains, not targeted by any of the currently available pneumococcal vaccines, has been rising as they seem to play an important role in the evolution of the species. Currently, there is a paucity of data regarding this group of pneumococci. Also, questions have been raised on whether they are true pneumococci. We aimed to obtain insights in the genetic content of NT and the mechanisms leading to non-typeability and to genetic diversity. Methods. A collection of 52 NT isolates representative of the lineages circulating in Portugal between 1997 and 2007, as determined by pulsed-field gel electrophoresis and multilocus sequence typing, was analysed. The capsular region was sequenced and comparative genomic hybridisation (CGH) using a microarray covering the genome of 10 pneumococcal strains was carried out. The presence of mobile elements was investigated as source of intraclonal variation. Results. NT circulating in Portugal were found to have similar capsular regions, of cps type NCC2, i.e., having aliB-like ORF1 and aliB-like ORF2 genes. The core genome of NT was essentially similar to that of encapsulated strains. Also, competence genes and most virulence genes were present. The few virulence genes absent in all NT were the capsular genes, type-I and type-II pili, choline-binding protein A (cbpA/pspC), and pneumococcal surface protein A (pspA). Intraclonal variation could not be entirely explained by the presence of prophages and other mobile elements. Conclusions. NT circulating in Portugal are a homogeneous group belonging to cps type NCC2. Our observations support the theory that they are bona-fide pneumococcal isolates that do not express the capsule but are otherwise essentially similar to encapsulated pneumococci. Thus we propose that NT should be routinely identified and reported in surveillance studies.
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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.
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Project description:Primary Sjögren’s syndrome (pSS) is the second most frequent systemic autoimmune disease, affecting 0.1% of the general population. No specific immunomodulatory drug has demonstrated efficacy for this disease, and no biomarker is available to identify patients at risk of developing systemic complications. To characterize the molecular and clinical variability across pSS patients, we integrated transcriptomic, proteomic, cellular and genetic data with clinical phenotypes in a cohort of 351 pSS patients. Unbiased global transcriptomic analysis revealed an IFN gene signature as the strongest driver of transcriptomic variability. The resulting stratification was replicated in three independent cohorts. As transcriptomic analysis did not discriminate between type I and II interferons, we applied digital ELISA to find that the IFN transcriptomic signature was driven by circulating IFNɑ protein levels. This cytokine, detectable in 75% of patients, was significantly associated with clinical and immunological features of disease activity at enrollment, and with increased frequency of systemic complications during the 5-year follow-up. Genetic analysis revealed a significant association between IFNɑ protein levels and an MHC-II HLA-DQ locus and anti-SSA antibody. Additional cellular analysis revealed that the polymorphism acts through upregulation of HLA II molecules on conventional DCs. Our unbiased analysis thus identified the predominance of IFNα as driver of pSS variability, and revealed an association with HLA gene polymorphisms.
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