Gribkova, S. | bioRxiv Science

bioRxiv2019

Transcriptional programs define intratumoral heterogeneity of Ewing sarcoma at single cell resolution

EWSR1-FLI1, the chimeric oncogene specific for Ewing sarcoma (EwS), induces a cascade of signaling events leading to cell transformation. However, it remains elusive how genetically homogeneous EwS cells can drive heterogeneity of transcriptional programs. Here, we combined independent component analysis of single cell RNA-sequencing data from diverse cell types and model systems with time-resolved mapping of EWSR1-FLI1 binding sites and of open chromatin regions to characterize dynamic cellular processes associated with EWSR1-FLI1 activity. We thus defined an exquisitely specific and direct, super-enhancer-driven EWSR1-FLI1 program. In EwS tumors, cell proliferation was associated with a well-defined range of EWSR1-FLI1 activity; moreover, cells with a high EWSR1-FLI1 activity presented a strong oxidative phosphorylation metabolism. In contrast, a subpopulation of cells from below and above optimal EWSR1-FLI1 activity was characterized by increased hypoxia. Overall, our study reveals sources of intratumoral heterogeneity within Ewing tumors.

cancer biology↗

bioRxiv2017

ZINB-WaVE: A general and flexible method for signal extraction from single-cell RNA-seq data

Single-cell RNA sequencing (scRNA-seq) is a powerful high-throughput technique that enables researchers to measure genome-wide transcription levels at the resolution of single cells. Because of the low amount of RNA present in a single cell, some genes may fail to be detected even though they are expressed; these genes are usually referred to as dropouts. Here, we present a general and flexible zero-inflated negative binomial model (ZINB-WaVE), which leads to low-dimensional representations of the data that account for zero inflation (dropouts), over-dispersion, and the count nature of the data. We demonstrate, with simulated and real data, that the model and its associated estimation procedure are able to give a more stable and accurate low-dimensional representation of the data than principal component analysis (PCA) and zero-inflated factor analysis (ZIFA), without the need for a preliminary normalization step.

bioinformatics↗