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Single-Cell Topological RNA-Seq Analysis Reveals Insights Into Cellular Differentiation and Development
Abbas H. Rizvi, Pablo G. Camara, Elena K. Kandror, Thomas J. Roberts, Ira Schieren, Tom Maniatis, Raul Rabadan
Transcriptional programs control cellular lineage commitment and differentiation during development. Understanding cell fate has been advanced by studying single-cell 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. Compared to other methods, scTDA is a non-linear, 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 non-coding RNAs. scTDA can be applied to study asynchronous cellular responses to either developmental cues or environmental perturbations.
Disease Model of GATA4 Mutation Reveals Transcription Factor Cooperativity in Human Cardiogenesis
Yen-Sin Ang, Renee N. Rivas, Alexandre JS Ribeiro, Rohith Srivas, Janell Rivera, Nicole R. Stone, Karishma Pratt, Tamer MA Mohamed, Ji-Dong Fu, C. Ian Spencer