A single-cell survey of cellular hierarchy in acute myeloid leukemia

Basic information
Cell
200,288
Sample
49

Technology
Microwell-seq
Omics
scRNA-seq
Source
Bone Marrow

Dataset ID
32977829
Platform
HiSeq X Ten
Species
Human
Disease
AML,Healthy
Age range
18 - 71
Update date
2020-09-25
Summary

Background: Acute myeloid leukemia (AML) is a fatal hematopoietic malignancy and has a prognosis that varies with its genetic complexity. However, there has been no appropriate integrative analysis on the hierarchy of different AML subtypes. Methods: Using Microwell-seq, a high-throughput single-cell mRNA sequencing platform, we analyzed the cellular hierarchy of bone marrow samples from 40 patients and 3 healthy donors. We also used single-cell single-molecule real-time (SMRT) sequencing to investigate the clonal heterogeneity of AML cells. Results: From the integrative analysis of 191727 AML cells, we established a single-cell AML landscape and identified an AML progenitor cell cluster with novel AML markers. Patients with ribosomal protein high progenitor cells had a low remission rate. We deduced two types of AML with diverse clinical outcomes. We traced mitochondrial mutations in the AML landscape by combining Microwell-seq with SMRT sequencing. We propose the existence of a phenotypic "cancer attractor" that might help to define a common phenotype for AML progenitor cells. Finally, we explored the potential drug targets by making comparisons between the AML landscape and the Human Cell Landscape. Conclusions: We identified a key AML progenitor cell cluster. A high ribosomal protein gene level indicates the poor prognosis. We deduced two types of AML and explored the potential drug targets. Our results suggest the existence of a cancer attractor.

Overall design

We applied Micro-well seq, a high-throughput and low-costing single-cell RNA platform, to 40 patients with 3 healthy donors. We identified a key subgroup and characterized its gene expression pattern from diagnosis to relapse, revealing its features in refractory patients. In addition, we combined next generation and SMRT sequencing to track the clonal evolution.

Contributors

To be supplemented.

Contact

ggj@zju.edu.cn.(Guoji Guo)

snRNA-Seq
Sample nameSample titleDiseaseGenderAgeSourceTreatmentTechnologyPlatformOmicsSample IDDataset IDAction
No data available