Single-Cell RNA-Seq Reveals AML Hierarchies Relevant to Disease Progression and Immunity

Abstract

Graphical Abstract Highlights d Technology for high-throughput single-cell RNA sequencing and genotyping d Variable cell-type composition of AML correlates to genetics and outcome d Primitive AML cells aberrantly co-express stemness and myeloid priming genes d Differentiated AML cells express immunomodulatory factors and suppress T cells In BriefA combination of transcriptomics and mutational analyses in single cells from acute myeloid leukemia patients reveals the existence of distinct functional subsets and their associated drivers. SUMMARYAcute myeloid leukemia (AML) is a heterogeneous disease that resides within a complex microenvironment, complicating efforts to understand how different cell types contribute to disease progression. We combined single-cell RNA sequencing and genotyping to profile 38,410 cells from 40 bone marrow aspirates, including 16 AML patients and five healthy donors. We then applied a machine learning classifier to distinguish a spectrum of malignant cell types whose abundances varied between patients and between subclones in the same tumor. Cell type compositions correlated with prototypic genetic lesions, including an association of FLT3-ITD with abundant progenitor-like cells. Primitive AML cells exhibited dysregulated transcriptional programs with co-expression of stemness and myeloid priming genes and had prognostic significance. Differentiated monocyte-like AML cells expressed diverse immunomodulatory genes and suppressed T cell activity in vitro. In conclusion, we provide single-cell technologies and an atlas of AML cell states, regulators, and markers with implications for precision medicine and immune therapies.

Capture at the single cell level of metabolic modules distinguishing aggressive and indolent glioblastoma cells

“…This predominant grouping of cancer cells by their tumor of origin has been reported for a number of cerebral and non-cerebral tumors [11,14,33,37,50,54,61]. For identifying traits common to all tumors, data can be analyzed tumor per tumor [62,63,69], or merged and analyzed as a whole after standardization (i.e. subtracting from each expression value the gene expression mean and dividing by its standard deviation across cells within a given tumor) [48].…”

Single-Cell Virtual Cytometer allows user-friendly and versatile analysis and visualization of multimodal single cell RNAseq datasets

“…Likewise, Single-Cell Virtual Cytometer is broadly applicable to the visualization of any kind of readout from any multimodal single cell technology, after adequate integration of the data sets (8). Its versatility enables users to analyze likewise any kind of single cell data about chromatin accessibility (9, 10), epigenomics (11), mutations (12), chromosome conformation (13), RNA modification (14), spatial transcriptomics (15,16) and spatial proteomics (17,18). Hence, Single-Cell Virtual Cytometer represents a unvaluable resource for integrated analyses of multimodal datasets at the single cell level.…”

Increased expression of genetically-regulatedFLT3implicated in Tourette’s Syndrome

“…Validation of expression in LCL prepared from TS cases found an increased or an increase in RNA expression compared to LCL from control individuals. The FLT3 gene encodes for a tyrosineprotein kinase and it has been associated with inflammation and immune function 13,14 . This could point towards immunity in TS as a putative biological mechanism.…”