Revealing the intratumoral heterogeneity of non-DS acute megakaryoblastic leukemia in single-cell resolution

Abstract

Pediatric acute megakaryoblastic leukemia (AMKL) is a subtype of acute myeloid leukemia (AML) characterized by abnormal megakaryoblasts, and it is divided into the AMKL patients with Down syndrome (DS-AMKL) and AMKL patients without DS (non-DS-AMKL). Pediatric non-DS-AMKL is a heterogeneous disease with extremely poor outcome. We performed single-cell RNA sequencing (scRNA-seq) of the bone marrow from two CBFA2T3-GLIS2 fusion-positive and one RBM15-MKL1 fusion-positive non-DS-AMKL children. Meanwhile, we downloaded the scRNA-seq data of normal megakaryocyte (MK) cells of the fetal liver and bone marrow from healthy donors as normal controls. We conducted cell clustering, cell-type identification, inferCNV analysis, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Monocle2 analysis to investigate the intratumoral heterogeneity of AMKL. Using canonical markers, we identified and characterized the abnormal blasts and other normal immune cells from three AMKL samples. We found intratumoral heterogeneity of AMKL in various cell-type proportions, malignant cells’ diverse copy number variations (CNVs), maturities, significant genes expressions, and enriched pathways. We also identified potential markers for pediatric AMKL, namely, RACK1, ELOB, TRIR, NOP53, SELENOH, and CD81. Our work offered insight into the heterogeneity of pediatric acute megakaryoblastic leukemia and established the single-cell transcriptomic landscape of AMKL for the first time.

Accumulation of STR-Loci Aberrations in Subclones of Jurkat Cell Line as a Model of Tumor Clonal Evolution

“…Su et al studied transcriptome landscape of childhood acute megakaryoblastic leukemia (AMKL), an extremely unfavorable variant of AML, using the scRNA-seq (single-cell RNA sequencing) technique. ITH in AMKL was found in different tumor markers and different patterns of DNA copy number variations (CNV) [ 30 ].…”

Innovative prognostic modeling in ESCC: leveraging scRNA-seq and bulk-RNA for dendritic cell heterogeneity analysis

“…Subsequently, the analysis of intercellular communication was conducted using the ‘CellChat’ R package ( 21 ). Following this, we performed single-cell regulatory network inference and clustering (SCENIC) analysis on DC cells using the ‘Scenic’ R package ( 22 ) Additionally, the malignancy level of DC cells was inferred using the copykat R package ( 23 ), and pseudotime analysis was conducted with the ‘monocle2’ R package ( 24 ). Finally, we calculated marker genes between three types of dendritic cells using the FindallMarkers function, with selection parameters including logfc.threshold = 1, min.pct = 0.25, only.pos = T. Furthermore, the ‘SCP’ R package was used during the data visualization process.…”