Dalong Qian

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.

Cancers originally develop from normal cells that gain the ability to proliferate aberrantly and eventually turn malignant. These cancerous cells then grow clonally into tumors and eventually have the potential to metastasize. A central question in cancer biology is, which cells can be transformed to form tumors? Recent studies elucidated the presence of cancer stem cells that have the exclusive ability to regenerate tumors. These cancer stem cells share many characteristics with normal stem cells, including self-renewal and differentiation. With the growing evidence that cancer stem cells exist in a wide array of tumors, it is becoming increasingly important to understand the molecular mechanisms that regulate self-renewal and differentiation because corruption of genes involved in these pathways likely participates in tumor growth. This new paradigm of oncogenesis has been validated in a growing list of tumors. Studies of normal and cancer stem cells from the same tissue have shed light on the ontogeny of tumors. That signaling pathways such as Bmi1 and Wnt have similar effects in normal and cancer stem cell self-renewal suggests that common molecular pathways regulate both populations. Understanding the biology of cancer stem cells will contribute to the identification of molecular targets important for future therapies.