Single-cell topological RNA-seq analysis reveals insights into cellular differentiation and development

• Published in: Nature biotechnology Vol. 35; no. 6; pp. 551 - 560
• Main Authors: Rizvi, Abbas H, Camara, Pablo G, Kandror, Elena K, Roberts, Thomas J, Schieren, Ira, Maniatis, Tom, Rabadan, Raul
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.06.2017; Nature Publishing Group
• Subjects: 38/39; 38/91; 45; 631/114/2408; 631/61/212/2019; Agriculture; Algorithms; Animals; Bioinformatics; Biomedical Engineering/Biotechnology; Biomedicine; Biotechnology; Cell cycle; Cell differentiation; Cell Differentiation - genetics; Cells, Cultured; Data analysis; Embryonic Stem Cells - cytology; Embryonic Stem Cells - physiology; Epithelial cells; Gene expression; Gene Expression Regulation - genetics; Genetic aspects; Life Sciences; Methods; Mice; Motor neurone disease; Motor Neurons - cytology; Motor Neurons - physiology; RNA - genetics; RNA sequencing; Sequence Analysis, RNA - methods; Single-Cell Analysis - methods; Spinal cord; Stem cells; Topology; Transcription, Genetic - genetics; Transcriptional Activation - genetics; New York; United States > US
• ISSN: 1087-0156; 1546-1696; 1546-1696
• DOI: 10.1038/nbt.3854

Analysis of RNA-seq data from individual developing mouse motor neuron cells with topological data analysis sheds light on crucial cell-fate decisions during neurogenesis. Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding of cell fate has been advanced by studying single-cell RNA-sequencing (RNA-seq) but is limited by the assumptions of current analytic methods regarding the structure of data. We present single-cell topological data analysis (scTDA), an algorithm for topology-based computational analyses to study temporal, unbiased transcriptional regulation. Unlike other methods, scTDA is a nonlinear, model-independent, unsupervised statistical framework that can characterize transient cellular states. We applied scTDA to the analysis of murine embryonic stem cell (mESC) differentiation in vitro in response to inducers of motor neuron differentiation. scTDA resolved asynchrony and continuity in cellular identity over time and identified four transient states (pluripotent, precursor, progenitor, and fully differentiated cells) based on changes in stage-dependent combinations of transcription factors, RNA-binding proteins, and long noncoding RNAs (lncRNAs). scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.