Abstract
The ability to quantify differentiation potential of single cells is a task of critical importance. Here we demonstrate, using over 7,000 single-cell RNA-Seq profiles, that differentiation potency of a single cell can be approximated by computing the signalling promiscuity, or entropy, of a cell’s transcriptome in the context of an interaction network, without the need for feature selection. We show that signalling entropy provides a more accurate and robust potency estimate than other entropy-based measures, driven in part by a subtle positive correlation between the transcriptome and connectome. Signalling entropy identifies known cell subpopulations of varying potency and drug resistant cancer stem-cell phenotypes, including those derived from circulating tumour cells. It further reveals that expression heterogeneity within single-cell populations is regulated. In summary, signalling entropy allows in silico estimation of the differentiation potency and plasticity of single cells and bulk samples, providing a means to identify normal and cancer stem-cell phenotypes.
Highlights
The ability to quantify differentiation potential of single cells is a task of critical importance
We propose that differentiation potential can be estimated in silico by integrating a cell’s transcriptomic profile with a high quality protein–protein interaction (PPI) network to define a cell-specific probabilistic signalling process on the network (Methods)
This quantifies the efficiency, or speed, with which signalling can diffuse over the whole network, and measures the number of separate biological processes which are in some sense ‘active’
Summary
The ability to quantify differentiation potential of single cells is a task of critical importance. Attesting to its general nature and broad applicability, we compute and validate signalling entropy in over 7,000 single cells of variable degrees of differentiation potency and phenotypic plasticity, including time-course differentiation data, neoplastic cells and circulating tumour cells (CTCs) This extends entropy concepts that we have previously demonstrated to work on bulk tissue data[9,11,12,13] to the single-cell level. On the basis of signalling entropy, we develop a novel algorithm called single-cell entropy (SCENT), which can be used to identify and quantify biologically relevant expression heterogeneity in single-cell populations, as well as to reconstruct cell-lineage trajectories from time-course data In this regard, SCENT differs substantially from other single-cell algorithms like Monocle[14], MPath[15], SCUBA16, Diffusion Pseudotime[17] or StemID18, in that it uses single-cell entropy to independently order single cells in pseudo-time (that is, differentiation potency), without the need for feature selection or clustering
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