Abstract
Emerging single-cell technologies have been pivotal in uncovering an extensive degree of heterogeneity between and within tissues (1). Analysis of single-cell data has shed light on many different cellular processes (2-7) and recent technological advances have enabled the study of a large number of parameters in single cells at unparalleled resolution. One such technology, mass cytometry (8), can measure up to 45 parameters simultaneously in tens of thousands of individual cells. Using mass cytometry and genomic sequencing of conventionally sorted subpopulations show that acute myelogenous leukemia (AML) in a given patient can simultaneously occupy multiple stages of differentiation. Occupation of these stages was correlated with the presence, or absence, of unique exonic mutation fingerprints. In another cancer, B-cell acute lymphoblastic leukemia (ALL), outgrowth of tumor at pro and pre-B cell stages was nearly always uniquely at a single stage - contrary to the results in AML. This suggests that evolutionary “niche” searching is not only for physical space in cancers, but also involves utilization of differentiation machinery as an additional elaboration mechanism. Each differentiation stage in both AML and B-cell ALL was characterized by utilization of cognate signaling networks which showed differential susceptibility to drug action. Using such deep profiling and signaling delineation approaches at the single-cell level will allow for fine structured indexing of patient disease and further tailoring of disease management. In addition, it will allow “heterogeneous” tumors to be organized by a maturation index associated with a granular catalog of mutations that drive cells to occupy these pseudo-differentiation niches.
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Disclosures:
No relevant conflicts of interest to declare.
McSNAC: A software to approximate first-order signaling networks from mass cytometry data