Homo sapiens strain:Single cancer cell
Ontology highlight
ABSTRACT:
PROVIDER: PRJDB11367 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| DRR278877.fastq.gz | Fastqsanger.gz | |||
| DRR278878.fastq.gz | Fastqsanger.gz | |||
| DRR278879.fastq.gz | Fastqsanger.gz | |||
| DRR278880.fastq.gz | Fastqsanger.gz | |||
| DRR278881.fastq.gz | Fastqsanger.gz |
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Project description:The ability to interrogate circulating tumor cells (CTC) and disseminated tumor cells (DTC) is restricted by the small number detected and isolated (typically <10). We wanted to determine if a commercially available technology could provide a transcriptomic profile of a single prostate cancer (PCa) cell. We clonally selected and cultured a single passage of cell cycle synchronized C4-2B PCa cells. Ten sets of single, 5-, or 10-cells were isolated using a micromanipulator under direct visualization with an inverted microscope. Additionally, we analyzed 10 individual DTC isolated from each of 2 patients with metastatic PCa. We have shown that a transcriptomic profile can be obtained from a single cell using commercially available technology. As expected, fewer amplified genes are detected from a single-cell sample than from pooled cell samples, but this method can be used to obtain a transcriptomic profile from DTC isolated from the bone marrow of patients with PCa. Custom Agilent 44K whole human genome expression oligonucleotide microarrays were used to profile clonally selected and cultured single passage of cell cycle synchronized C4-2B PCa cells isolated using a micromanipulator under direct visualization with an inverted microscope into ten sets of single, 5-, or 10-cells. Single disseminated tumor cells were isolated from bone marrow (BM) samples of two advanced prostate cancer patients. Essentially, a two-step selection process was employed, in which anti-CD45 and anti-CD61 conjugated to immunomagnetic beads were used for negative selection, and anti-HEA was used for positive selection. Cells were then fluorescently stained for BerEP4, counter stained with RPE anti-CD45, and individually selected (10 single cells each per patient) under fluorescent light using a micropipette system for further analysis. RNA was amplified using the WT-Oviation one-direct system and hybridization against a common reference pool of prostate tumor cell lines. Data from C42B cell data and data from single cells isolated from the bone marrow of patients were normalized and analyzed separately.
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Project description:In this comprehensive study, the authors have developed concise models integrating clinical, genomic and transcriptomic features to predict intrinsic resistance to anti-PD1 Immune Checkpoint Blockade (ICB) treatment in individual tumors. It's important to note that their validation was performed in smaller, independent cohorts, constrained by data availability. The authors have developed two Logistic Regression based models for Ipilimumab treated and Ipilimumab naive patients with metastatic melanoma. The main predictive features for the Ipilimumab treated patients are MHC-II HLA, LDH at treatment initiation and the presence of lymph node metastases (LN met), chosen using forward selection methodology. The main predictive features for the Ipilimumab naive patients are tumor heterogeneity, tumor ploidy and tumor purity, chosen using forward selection methodology.
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2023-11-17 | MODEL2310150001 | BioModels
Project description:The ability to interrogate circulating tumor cells (CTC) and disseminated tumor cells (DTC) is restricted by the small number detected and isolated (typically <10). We wanted to determine if a commercially available technology could provide a transcriptomic profile of a single prostate cancer (PCa) cell. We clonally selected and cultured a single passage of cell cycle synchronized C4-2B PCa cells. Ten sets of single, 5-, or 10-cells were isolated using a micromanipulator under direct visualization with an inverted microscope. Additionally, we analyzed 10 individual DTC isolated from each of 2 patients with metastatic PCa. We have shown that a transcriptomic profile can be obtained from a single cell using commercially available technology. As expected, fewer amplified genes are detected from a single-cell sample than from pooled cell samples, but this method can be used to obtain a transcriptomic profile from DTC isolated from the bone marrow of patients with PCa.
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Project description:The bone marrow (BM) is the third most frequent site of metastasis for solid tumors, creating an unfavorable clinical outcome. It provides a unique microenvironment that promotes growth of tumors, however, the role of different BM cells, their molecular features, and their interactions with tumor cells, are poorly defined. Here, we investigate the BM niche in neuroblastoma (NB), a pediatric cancer of the sympathetic nervous system. NB has been molecularly defined at the primary cancer site, yet, the metastatic site is poorly characterized. We performed single-cell transcriptomics (scRNA-seq) and epigenomic profiling (scATAC-seq) of BM aspirates from 11 subjects spanning three major NB subtypes: patients with MYCN amplification (MNA), ATRX mutations (ATRXmut), and cases that lack these alterations (sporadic): NB cases were then compared to five age-matched and metastasis-free BM (controls), followed by in-depth single cell analyses of tissue diversity and cell-cell interactions. We present the first map of the epigenetic and transcriptomic effects of bone marrow metastases. Our analyses demonstrate that tumor cells in the metastatic niche display plasticity that differs among NB subtypes. NB cells via cell-cell interaction signal to the bone marrow microenvironment, rewiring specifically monocytes, which exhibit M1 and M2 features, marked by activation of pro- and anti-inflammatory programs, and express tumor-promoting factors, reminiscent of tumor-associated macrophages. Our study may provide the basis for a therapeutic approach, targeting tumor-to-microenvironment interactions.
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Project description:<p>Circulating tumor cells (CTCs) are recognized as direct seeds of metastasis. However, CTC count may not be the 'best' indicator of metastatic risk because their heterogeneity is generally neglected. In this study, we develop a molecular typing system to predict colorectal cancer metastasis potential based on the metabolic fingerprints of single CTCs. After identification of the metabolites potentially related to metastasis using mass spectrometry-based untargeted metabolomics, setup of a home-built single-cell quantitative mass spectrometric platform for target metabolite analysis in individual CTCs and use of a machine learning method composed of non-negative matrix factorization and logistic regression, CTCs are divided into two subgroups, C1 and C2, based on a 4-metabolite fingerprint. Both <em>in vitro</em> and <em>in vivo</em> experiments demonstrate that CTC count in C2 subgroup is closely associated with metastasis incidence. This is an interesting report on the presence of a specific population of CTCs with distinct metastatic potential at the single-cell metabolite level. </p>
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