Single-celled RNAseq differentiates cancer stem cells from healthy stem cells

Acute myeloid leukemia is a good model for studying cancer stem cells because 10% to 20% of people over 70 years of age develop preukemic mutations in hematopoietic stem cells. From there, preleukemic stem cells can give rise to healthy blood and immune cells, but acquiring additional mutations could lead to a malignant expansion of aberrant leukemic stem cells.

These cancer stem cells can fuel long-term cancer growth through their ability to regenerate continuously. However, the low division rates make them difficult to target therapeutically. Chemotherapy often misses the leukemia stem cells, which leads to a recurrent relapse. Therefore, the identification of therapeutic strategies targeting leukemic stem cells while sparing healthy hematopoietic stem cells is urgently needed.

Researchers at the Center for Genomic Regulation (CRG) and the European Molecular Biology Laboratory (EMBL) sought to develop an approach that could characterize the differences in gene expression between healthy hematopoietic stem cells, preleukemia stem cells and cells strains of leukemia. Together, MutaSeq amplifies nuclear mutations from complementary DNA (cDNA) and mitoClone, a computational tool that achieves high-confidence clonal assignments and de novo clone discovery using mitochondrial marker mutations when available. , allowed the team to use genomic and mitochondrial mutations to distinguish between healthy and cancerous cells.

They simultaneously measured mutational status and gene expression in individual cells in order to distinguish cancer stem cells from mature cancer cells (based on gene expression) and healthy stem cells (based on mutational status) . In addition, they were able to distinguish preleukemia stem cells through the detection of clonal hematopoiesis of undetermined potential mutations (CHIP).

To explore the applications of MutaSeq in combination with mitoClone, the researchers generated data from four patients with acute myeloid leukemia with heterogeneous genotypes and phenotypes. The final data set consisted of 618 to 1,430 cells per patient, of which 190 to 968 were CD34 + (an indicator of acute myeloid leukemia).

The researchers then grouped the cells into clonal hierarchies (progeny relationships between different clones that reveal the order in which mutations were acquired) based on nuclear genomic mutations, as well as mitochondrial mutations.

This technique revealed known preleukemia mutations, such as mutations in the SRSF2 and DNMT3A genes. However, relying on somatic nuclear mutations failed to identify other mutations with lower expression. For these, the team used mitochondrial mutations to refine clonal hierarchies as well as nuclear mutations. In two of the samples, they found that preleukemic, as well as subclonal, leukemic mutations were significantly associated with distinct sets of well-covered mitochondrial variants.

Among the four patients, the researchers identified clones carrying known preukemia mutations (in SRSF2 and DNMT3A) and subclones carrying known leukemia mutations (CEBPA and NPM1).

On a de novo basis, the researchers used MutaSeq to identify clones associated with leukemic stem cells. They found a highly significant association of the P95H mutation of serine and arginine-rich splicing factor 2 (SRSF2) with the leukemic and preleukemic subclones and a mutation in DNA methyltransferase 3A (DNMT3A) associated with a mutation unbound in the RPL3 gene.

An image of stem cells

An image of stem cells (blue) in the bone marrow with a sinusoidal blood vessel (red) and an arteriolar blood vessel. Image courtesy of Jude Al-Sabah / DKFZ.

Then, they used single-cell transcriptome data to distinguish stem cells, progenitor cells, and leukemic blasts (white blood cells in the bone marrow). Transcription factors, such as activator protein 1 (AP1), appear to be relevant in all phenotypically very different blast populations.

They integrated unicellular gene expression data and clonal tracking results to characterize cells as (pre-) leukemic stem cells or healthy cells. Importantly, the MutaSeq data allowed comparison of gene expression between clones differing only by a single mutation. This allowed researchers to determine the specific effects of a single mutation in hematopoiesis.

They found that mutations in the de novo DNA methyl transferase, DNMT3A, are the most common cause of benign clonal expansions of healthy stem cells in older individuals. In addition, mutations in multifunctional ribonucleoprotein 1 (NPM1) are identified as factors of acute myeloid leukemia in 30% of patients and frequently co-occur with preleukemic DNMT3A mutations.

Finally, the researchers compared gene expression between all (pre-) leukemic and non-leukemic cells, with the aim of identifying potential markers or drug targets present in all (pre-) leukemic cells, but not in residual healthy cells.

“There are a large number of small molecule drugs that have been shown to be clinically safe, but deciding which cancers and more precisely for which patients these drugs are well suited is a difficult task,” says author Lars Steinmetz, PhD, Stanford University professor group leader at EMBL Heidelberg, in a statement. “Our method can identify drug targets that may not have been tested in the right setting. These tests will need to be done in controlled clinical studies, but knowing what to try is an important first step.”

“We have now brought together clinical researchers from Germany and Spain to apply this method in much larger clinical studies,” said author Lars Velten, group leader at CRG. “We are also streamlining the method. Our vision is to individually identify drug targets specific to cancer stem cells, which allows patients and physicians to research these treatments as easily as testing for coronavirus.”

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