Single-cell transcriptional diversity is a hallmark of developmental potential

By G. Gulati, Shaheen S. Sikandar, Daniel J. Wesche, A. Manjunath, A. Bharadwaj, Mark J. Berger, Francisco Ilagan, Angera H. Kuo, Robert W. Hsieh, S. Cai and 8 more. Science, 2019

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More diversity at the top A detailed knowledge of cell differentiation hierarchies is important for understanding diverse biological processes such as organ development, tissue regeneration, and cancer. Single-cell RNA sequencing can help elucidate these hierarchies, but it requires reliable computational methods for predicting cell lineage trajectories. Gulati et al. developed CytoTRACE, a computational framework based on the simple observation that transcriptional diversity—the number of genes expressed in a cell—decreases during differentiation. CytoTRACE outperformed other methods in several test cases and was successfully applied to study cellular hierarchies in healthy and tumor tissue. Science, this issue p. 405 The number of expressed genes per cell is the foundation of a method that can resolve cell differentiation hierarchies. Single-cell RNA sequencing (scRNA-seq) is a powerful approach for reconstructing cellular differentiation trajectories. However, inferring both the state and direction of differentiation is challenging. Here, we demonstrate a simple, yet robust, determinant of developmental potential—the number of expressed genes per cell—and leverage this measure of transcriptional diversity to develop a computational framework (CytoTRACE) for predicting differentiation states from scRNA-seq data. When applied to diverse tissue types and organisms, CytoTRACE outperformed previous methods and nearly 19,000 annotated gene sets for resolving 52 experimentally determined developmental trajectories. Additionally, it facilitated the identification of quiescent stem cells and revealed genes that contribute to breast tumorigenesis. This study thus establishes a key RNA-based feature of developmental potential and a platform for delineation of cellular hierarchies.

Subfields: Cancer Research, Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/7ca2a15b28320291edc8797c1bb6dd6e8fa9b6c8