Single-cell analysis of childhood leukemia reveals a link between developmental states and ribosomal protein expression as a source of intra-individual heterogeneity

Basic information
Cell
39,375
Sample
11

Technology
10X Genomics
Omics
scRNA-seq
Source
Bone Marrow

Dataset ID
32415257
Platform
Illumina HiSeq 4000
Species
Human
Disease
cALL,Healthy
Age range
0 - 0
Update date
2020-05-15
Summary

Childhood acute lymphoblastic leukemia (cALL) is the most common pediatric cancer. It is characterized by bone marrow lymphoid precursors that acquire genetic alterations, resulting in disrupted maturation and uncontrollable proliferation. More than a dozen molecular subtypes of variable severity can be used to classify cALL cases. Modern therapy protocols currently cure 85-90% of cases, but other patients are refractory or will relapse and eventually succumb to their disease. To better understand intratumor heterogeneity in cALL patients, we investigated the nature and extent of transcriptional heterogeneity at the cellular level by sequencing the transcriptomes of 39,375 individual cells in eight patients (six B-ALL and two T-ALL) and three healthy pediatric controls. We observed intra-individual transcriptional clusters in five out of the eight patients. Using pseudotime maturation trajectories of healthy B and T cells, we obtained the predicted developmental state of each leukemia cell and observed distribution shifts within patients. We showed that the predicted developmental states of these cancer cells are inversely correlated with ribosomal protein expression levels, which could be a common contributor to intra-individual heterogeneity in cALL patients.

Overall design

10X Genomics 3’ single cell RNA-seq: Pre-B t(12;21) [ETV6-RUNX1] acute lymphoblastic leukemia, Pre-B High hyper diploid [HHD] acute lymphoblastic leukemia, Pre-T acute lymphoblastic leukemia, Healthy pediatric bone marrow mononuclear cells. Annotations.tsv contains sample, cell cycle phase, tSNE/UMAP coordinates, and assigned cell type.

Contributors

Maxime Caron†, Daniel Sinnett✉️, Guillaume Bourque✉️

Contact

To be supplemented.

snRNA-Seq
Sample nameSample titleDiseaseGenderAgeSourceTreatmentTechnologyPlatformOmicsSample IDDataset IDAction
No data available