A new method, described in a study published today in the journal Nature Communications, has the potential to boost international research efforts to find drugs that eradicate cancer at its source.
Most cancerous tissue consists of rapidly dividing cells with a limited capacity for self-renewal, meaning that the bulk of cells stop reproducing after a certain number of divisions. However, cancer stem cells can replicate indefinitely, fuelling long-term cancer growth and driving relapse.
Cancer stem cells that elude conventional treatments like chemotherapy are one of the reasons patients initially enter remission but relapse soon after. In acute myeloid leukaemia, a form of blood cancer, the high probability of relapse means fewer than 15% of elderly patients live longer than five years.
However, cancer stem cells are difficult to isolate and study because of their low abundance and similarity to other stem cells, hampering international research efforts in developing precision treatments that target malignant cells while sparing healthy ones.
Researchers from the Centre for Genomic Regulation (CRG) and the European Molecular Biology Laboratory (EMBL) have overcome this problem by creating MutaSeq, a method that can be used to distinguish cancer stem cells, mature cancer cells and otherwise healthy stem cells based on their genetics and gene expression.
“RNA provides vital information for human health. For example, PCR tests for coronavirus detect its RNA to diagnose COVID-19. Subsequent sequencing can determine the virus variant,” explains Lars Velten, Group Leader at the CRG and author of the paper. “MutaSeq works like a PCR test for coronavirus, but at a much more complex level and with a single cell as starting material.”
To determine if a single cell is a stem cell, the researchers used MutaSeq to measure thousands of RNAs at the same time. To then find out if the cell is cancerous or healthy, the researchers carried out additional sequencing and looked for mutations. The resulting data helped researchers track if stems cells are cancerous or healthy and helped determine what makes the cancer stem cells different.
MutaSeq for high-quality single-cell RNA-seq data with clonal information

a Overview of the study. HSC: hematopoietic stem cell, (pre-)LSC: (pre-)leukemic stem cell, Blast: mature leukemic blast. b Overview of the MutaSeq method. Targeting primers (purple) are included during the cDNA amplification step of the Smart-seq2 protocol. Targeting primers are directly fused to illumina library adapters (blue) and therefore get amplified efficiently during library preparation. Tagmentation introduces the same adapters to the full-length cDNA product.
“There are a huge number of small molecule drugs out there with demonstrated clinical safety, but deciding which cancers and more specifically which patients these drugs are well suited for is a daunting task,” says Lars Steinmetz, Professor at Stanford University, Group Leader at EMBL Heidelberg and author of the paper. “Our method can identify drug targets that might not have been tested in the right context. These tests will need to be carried out in controlled clinical studies, but knowing what to try is an important first step.”
The method is based on single cell sequencing, an increasingly common technique that helps researchers gather and interpret genome-wide information from thousands of individual cells. Single cell sequencing provides a highly detailed molecular profile of complex tissues and cancers, opening new avenues for research.
Explaining their next steps, Lars Velten says: “We have now brought together clinical researchers from Germany and Spain to apply this method in much larger clinical studies. We are also making the method much more streamlined. Our vision is to identify cancer stem cell specific drug targets in a personalized manner, making it ultimately as easy for patients and doctors to look for these treatments as it is testing for coronavirus”.
Source – Center for Genomic Regulation
Velten L, Story BA, Hernández-Malmierca P et al. (2020) Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics. Nat Comm [Epub ahead of print]. [article]
A new method, described in a study published today in the journal Nature Communications, has the potential to boost international research efforts to find drugs that eradicate cancer at its source.
Most cancerous tissue consists of rapidly dividing cells with a limited capacity for self-renewal, meaning that the bulk of cells stop reproducing after a certain number of divisions. However, cancer stem cells can replicate indefinitely, fuelling long-term cancer growth and driving relapse.
Cancer stem cells that elude conventional treatments like chemotherapy are one of the reasons patients initially enter remission but relapse soon after. In acute myeloid leukaemia, a form of blood cancer, the high probability of relapse means fewer than 15% of elderly patients live longer than five years.
However, cancer stem cells are difficult to isolate and study because of their low abundance and similarity to other stem cells, hampering international research efforts in developing precision treatments that target malignant cells while sparing healthy ones.
Researchers from the Centre for Genomic Regulation (CRG) and the European Molecular Biology Laboratory (EMBL) have overcome this problem by creating MutaSeq, a method that can be used to distinguish cancer stem cells, mature cancer cells and otherwise healthy stem cells based on their genetics and gene expression.
To determine if a single cell is a stem cell, the researchers used MutaSeq to measure thousands of RNAs at the same time. To then find out if the cell is cancerous or healthy, the researchers carried out additional sequencing and looked for mutations. The resulting data helped researchers track if stems cells are cancerous or healthy and helped determine what makes the cancer stem cells different.
MutaSeq for high-quality single-cell RNA-seq data with clonal information
a Overview of the study. HSC: hematopoietic stem cell, (pre-)LSC: (pre-)leukemic stem cell, Blast: mature leukemic blast. b Overview of the MutaSeq method. Targeting primers (purple) are included during the cDNA amplification step of the Smart-seq2 protocol. Targeting primers are directly fused to illumina library adapters (blue) and therefore get amplified efficiently during library preparation. Tagmentation introduces the same adapters to the full-length cDNA product.
The method is based on single cell sequencing, an increasingly common technique that helps researchers gather and interpret genome-wide information from thousands of individual cells. Single cell sequencing provides a highly detailed molecular profile of complex tissues and cancers, opening new avenues for research.
Source – Center for Genomic Regulation
Velten L, Story BA, Hernández-Malmierca P et al. (2020) Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics. Nat Comm [Epub ahead of print]. [article]
Related Posts
POND-seq enables non-destructive RNA sequencing in living cells
Worm’s radical transformation shows metamorphosis can change the functions of cells
New method allows scientists to follow gene activity over time in the same cells
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
A new method, described in a study published today in the journal Nature Communications, has the potential to boost international research efforts to find drugs that eradicate cancer at its source.
Most cancerous tissue consists of rapidly dividing cells with a limited capacity for self-renewal, meaning that the bulk of cells stop reproducing after a certain number of divisions. However, cancer stem cells can replicate indefinitely, fuelling long-term cancer growth and driving relapse.
Cancer stem cells that elude conventional treatments like chemotherapy are one of the reasons patients initially enter remission but relapse soon after. In acute myeloid leukaemia, a form of blood cancer, the high probability of relapse means fewer than 15% of elderly patients live longer than five years.
However, cancer stem cells are difficult to isolate and study because of their low abundance and similarity to other stem cells, hampering international research efforts in developing precision treatments that target malignant cells while sparing healthy ones.
Researchers from the Centre for Genomic Regulation (CRG) and the European Molecular Biology Laboratory (EMBL) have overcome this problem by creating MutaSeq, a method that can be used to distinguish cancer stem cells, mature cancer cells and otherwise healthy stem cells based on their genetics and gene expression.
To determine if a single cell is a stem cell, the researchers used MutaSeq to measure thousands of RNAs at the same time. To then find out if the cell is cancerous or healthy, the researchers carried out additional sequencing and looked for mutations. The resulting data helped researchers track if stems cells are cancerous or healthy and helped determine what makes the cancer stem cells different.
MutaSeq for high-quality single-cell RNA-seq data with clonal information
a Overview of the study. HSC: hematopoietic stem cell, (pre-)LSC: (pre-)leukemic stem cell, Blast: mature leukemic blast. b Overview of the MutaSeq method. Targeting primers (purple) are included during the cDNA amplification step of the Smart-seq2 protocol. Targeting primers are directly fused to illumina library adapters (blue) and therefore get amplified efficiently during library preparation. Tagmentation introduces the same adapters to the full-length cDNA product.
The method is based on single cell sequencing, an increasingly common technique that helps researchers gather and interpret genome-wide information from thousands of individual cells. Single cell sequencing provides a highly detailed molecular profile of complex tissues and cancers, opening new avenues for research.
Source – Center for Genomic Regulation
Velten L, Story BA, Hernández-Malmierca P et al. (2020) Identification of leukemic and pre-leukemic stem cells by clonal tracking from single-cell transcriptomics. Nat Comm [Epub ahead of print]. [article]
Related Posts
POND-seq enables non-destructive RNA sequencing in living cells
Worm’s radical transformation shows metamorphosis can change the functions of cells
New method allows scientists to follow gene activity over time in the same cells
Single-cell and single-embryo RNA sequencing
RNA sequencing reveals functional chimeric mRNAs in mammalian immunity
Deep learning improves microRNA target prediction from sequence
Atlas of the brain’s striatum could guide researchers to new drug treatments
scLS – a computationally efficient differentially expressed gene detection algorithm
Spatial mapping of RNA turnover kinetics in the mouse brain
Immune cells offer insights on billion-dollar virus
SPIDER improves spatial transcriptomics data using single-cell RNA sequencing
Ultrafast and reference-free sequence discovery in single-cell data
ARCADIA combines RNA sequencing and spatial proteomics to reveal how tissue location shapes cell behavior
An end-to-end computational framework for “Record-seq” transcriptional recording data
A functionally integrated cross-tissue alternative splicing program during short-term calorie restriction
ExoShorkie – predicting RNA-seq coverage of exogenous genomes in yeast by transfer learning
Dietary oxidized plant sterol shifts macrophage state to fuel aortic inflammation
Bonsai reconstructs tree representations for distortion-free visualization and exploration of high-dimensional data
MiRQuery – a user-friendly web app for the interactive analysis and visualization of microRNA sequencing data
RNA sequencing resolves cryptic pathogenic variants in mitochondrial disease
Stay Connected
Submit a Post to the Blog
Recent Posts
Subscribe to the RNA-Seq Blog
RNA-Seq Products & Services