Browse
Submit Data
Databases
API
Help

Dataset Information

15 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Colorectal Cancer Stem Cell States Uncovered by Simultaneous Single-Cell Analysis of Transcriptome and Telomeres.

ABSTRACT: Cancer stem cells (CSCs) presumably contribute to tumor progression and drug resistance, yet their definitive features have remained elusive. Here, simultaneous measurement of telomere length and transcriptome in the same cells enables systematic assessment of CSCs in primary colorectal cancer (CRC). The in-depth transcriptome profiled by SMART-seq2 is independently validated by high-throughput scRNA-seq using 10 × Genomics. It is found that rare CSCs exist in dormant state and display plasticity toward cancer epithelial cells (EPCs) that essentially are presumptive tumor-initiating cells (TICs), while both retaining the prominent signaling pathways including WNT, TGF-β, and HIPPO/YAP. Moreover, CSCs exhibit chromosome copy number variation (CNV) pattern resembling cancer EPCs but distinct from normal stem cells, suggesting the phylogenetic relationship between CSCs and cancer EPCs. Notably, CSCs maintain shorter telomeres and possess minimal telomerase activity consistent with their nonproliferative nature, unlike cancer EPCs. Additionally, the specific signature of CSCs particularly NOTUM, SMOC2, BAMBI, PHLDA1, and TNFRSF19 correlates with the prognosis of CRC. These findings characterize the heterogeneity of CSCs and their linkage to cancer EPCs/TICs, some of which are conventionally regarded as CSCs.

SUBMITTER: Wang H

PROVIDER: S-EPMC8061397 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Colorectal cancer stem cell states uncovered by simultaneous single-cell analysis of transcriptome and telomeres

Project description:Here, simultaneous measurement of telomere length and transcriptome in the same cells enable systematic assessment of CSCs in primary colorectal cancer (CRC). The in-depth transcriptome profiled by SMART-seq2 is independently validated by high-throughput scRNA-seq using 10x Genomics. We find that rare CSCs exist in dormant state and display plasticity towards cancer epithelial cells (EPCs) that essentially are presumptive tumor-initiating cells (TICs), while both retaining the prominent signaling pathways including WNT, TGF-b and HIPPO/YAP. Moreover, CSCs exhibit chromosome copy number variation (CNV) pattern resembling cancer EPCs but distinct from normal stem cells, suggesting the phylogenetic relationship between CSCs and cancer EPCs. Notably, CSCs maintain shorter telomeres and possess minimal telomerase activity consistent with their non-proliferative nature, unlike cancer EPCs.

2020-12-30 | GSE163974 | GEO

Colorectal cancer stem cell states uncovered by simultaneous single-cell analysis of transcriptome and telomeres

Project description:Colorectal cancer stem cell states uncovered by simultaneous single-cell analysis of transcriptome and telomeres

| PRJNA688461 | ENA

Single-cell and bulk transcriptome sequencing identifies two epithelial tumor cell states and refines the consensus molecular classification of colorectal cancer.

Project description:The consensus molecular subtype (CMS) classification of colorectal cancer is based on bulk transcriptomics. The underlying epithelial cell diversity remains unclear. We analyzed 373,058 single-cell transcriptomes from 63 patients, focusing on 49,155 epithelial cells. We identified a pervasive genetic and transcriptomic dichotomy of malignant cells, based on distinct gene expression, DNA copy number and gene regulatory network. We recapitulated these subtypes in bulk transcriptomes from 3,614 patients. The two intrinsic subtypes, iCMS2 and iCMS3, refine CMS. iCMS3 comprises microsatellite unstable (MSI-H) cancers and one-third of microsatellite-stable (MSS) tumors. iCMS3 MSS cancers are transcriptomically more similar to MSI-H cancers than to other MSS cancers. CMS4 cancers had either iCMS2 or iCMS3 epithelium; the latter had the worst prognosis. We defined the intrinsic epithelial axis of colorectal cancer and propose a refined 'IMF' classification with five subtypes, combining intrinsic epithelial subtype (I), microsatellite instability status (M) and fibrosis (F).

| S-EPMC9279158 | biostudies-literature

Simultaneous profiling of transcriptome and DNA methylome from a single cell.

Project description:BackgroundSingle-cell transcriptome and single-cell methylome technologies have become powerful tools to study RNA and DNA methylation profiles of single cells at a genome-wide scale. A major challenge has been to understand the direct correlation of DNA methylation and gene expression within single-cells. Due to large cell-to-cell variability and the lack of direct measurements of transcriptome and methylome of the same cell, the association is still unclear.ResultsHere, we describe a novel method (scMT-seq) that simultaneously profiles both DNA methylome and transcriptome from the same cell. In sensory neurons, we consistently identify transcriptome and methylome heterogeneity among single cells but the majority of the expression variance is not explained by proximal promoter methylation, with the exception of genes that do not contain CpG islands. By contrast, gene body methylation is positively associated with gene expression for only those genes that contain a CpG island promoter. Furthermore, using single nucleotide polymorphism patterns from our hybrid mouse model, we also find positive correlation of allelic gene body methylation with allelic expression.ConclusionsOur method can be used to detect transcriptome, methylome, and single nucleotide polymorphism information within single cells to dissect the mechanisms of epigenetic gene regulation.

| S-EPMC4858893 | biostudies-literature

Stem vs non-stem cell origin of colorectal cancer.

Project description:Colorectal cancer (CRC) is one of the most common cancers in the western world and is characterised by deregulation of the Wnt signalling pathway. Mutation of the adenomatous polyposis coli (APC) tumour suppressor gene, which encodes a protein that negatively regulates this pathway, occurs in almost 80% of CRC cases. The progression of this cancer from an early adenoma to carcinoma is accompanied by a well-characterised set of mutations including KRAS, SMAD4 and TP53. Using elegant genetic models the current paradigm is that the intestinal stem cell is the origin of CRC. However, human histology and recent studies, showing marked plasticity within the intestinal epithelium, may point to other cells of origin. Here we will review these latest studies and place these in context to provide an up-to-date view of the cell of origin of CRC.

| S-EPMC4647531 | biostudies-other

Single-cell transcriptome landscape in colorectal cancer

Project description:Single-cell transcriptome landscape in colorectal cancer

| PRJNA1163323 | ENA

Single-cell transcriptome analysis reveals heterogeneity and convergence of the tumor microenvironment in colorectal cancer.

Project description:IntroductionColorectal cancer (CRC) ranks second for mortality and third for morbidity among the most commonly diagnosed cancers worldwide. We aimed to investigate the heterogeneity and convergence of tumor microenvironment (TME) in CRC.MethodsWe analyzed the single-cell RNA sequencing data obtained from the Gene Expression Omnibus (GEO) database and identified 8 major cell types and 25 subgroups derived from tumor, para-tumor and peripheral blood.ResultsIn this study, we found that there were significant differences in metabolic patterns, immunophenotypes and transcription factor (TF) regulatory patterns among different subgroups of each major cell type. However, subgroups manifested similar lipid metabolic patterns, immunosuppressive functions and TFs module at the end of the differentiation trajectory in CD8+ T cells, myeloid cells and Fibroblasts. Meanwhile, TFs regulated lipid metabolism and immunosuppressive ligand-receptor pairs were detected by tracing the differentiation trajectory. Based on the cell subgroup fractions calculated by CIBERSORTx and bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA), we constructed an immune risk model and clinical risk model of CRC which presented excellent prognostic value.ConclusionThis study identified that the differentiation was accompanied by remodeling of lipid metabolism and suppression of immune function, which suggest that lipid remodeling may be an important trigger of immunosuppression. More importantly, our work provides a new perspective for understanding the heterogeneity and convergence of the TME and will aid the development of prognosis and immunotherapies of CRC patients.

| S-EPMC9845924 | biostudies-literature

Single-cell transcriptome highlights a multilayer regulatory network on an invasive trajectory within colorectal cancer progression.

Project description:PurposeColorectal cancer (CRC) is one of the most common and fatal gastrointestinal malignancies, in which cancer stem cells (CSCs) were identified to enable tumor heterogeneity and initiate tumor formation. However, the process from CSCs to invasion cells is unconfirmed.MethodsSeveral bioinformatics methods, including clustering, pseudotime analysis, gene set variation analysis and gene ontology enrichment, were used to construct a path of gradual transformation of CSCs to invasive cells, called "stem-to-invasion path". A large amount of signaling interactions were collated to build the multilayer regulatory network. Kaplan-Meier curve and time-dependent ROC method were applied to reveal prognostic values.ResultsWe validated the heterogeneity of cells in the tumor microenvironment and revealed the presence of malignant epithelial cells with high invasive potential within primary colonic carcinomas. Next, the "stem-to-invasion path" was identified through constructing a branching trajectory with cancer cells arranged in order. A multilayer regulatory network considered as the vital factor involved in acquiring invasion characteristics underlying the path was built to elucidate the interactions between tumor cell and tumor-associated microenvironment. Then we further identified a novel combinatorial biomarker that can be used to assess the prognosis for CRC patients, and validated its predictive robustness on the independent dataset.ConclusionOur work provides new insights into the acquisition of invasive potential in primary tumor cells, as well as potential therapeutic targets for CRC invasiveness, which may be useful for the cancer research and clinical treatment.

| S-EPMC9075720 | biostudies-literature

Simultaneous epitope and transcriptome measurement in single cells.

Project description:High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels. Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout. CITE-seq is compatible with existing single-cell sequencing approaches and scales readily with throughput increases.

| S-EPMC5669064 | biostudies-literature

Single-cell transcriptome landscape in colorectal cancer

Project description:One tumor tissue specimen pathologically diagnosed as adenocarcinoma undergoing radical colorectal surgery without any preoperative treatment at our center was collected, and the single cell transcriptome sequencing was performed, which was consistent with the inclusion and exclusion criteria for the prospective observational clinical study.The detailed procedures of pre-processing and quality control were as follows: (1) Raw data quality control; (2) Data comparison and mapping; (3) Cell Barcode Correction; (4) UMI disaggregation; (5) Count matrix generation; (6) Quality control indicators; (7) Data standardization and normalization; (8) Identification of highly variable genes (HVGs); (9) Filtering condition settings: nCount_RNA > 1000 & nFeature_RNA < 3000 & percent.mt < 10 & nFeature_RNA > 200. The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm was used for downscaling and cell clustering, and the "SingleR" R package was used for cell annotation. The "CellChat" and "NicheNet" R packages were used for cell communication. The "monocle2" was used for cell trajectory analysis.

2024-12-13 | GSE277669 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data