Time-resolved single-cell RNA-seq using metabolic RNA labelling

Abstract
Introduction
Results
	Development and validation of scNT-Seq
	Evaluating scNT-Seq performance for detecting activity-induced new RNAs
	Identification of neuronal activity-induced, time-resolved regulon activity
	Metabolic labeling-based time-resolved RNA velocity analysis
	scNT-Seq reveals distinct RNA regulatory strategies during stem cell state transition.
	Time-resolved regulon analysis reveals TET-mediated regulation of the pluripotent-to-2C transition
	Second strand synthesis reaction substantially enhances the efficiency of scNT-Seq
Discussion
Methods
	Mouse embryonic stem cell (mESC) cultures and metabolic labeling
	CRISPR-Cas9 genome editing in mESCs
	Human K562 cell cultures and species mixing experiments
	Mouse cortical neuronal culture and activity stimulation
	Cell fixation, cryopreservation and rehydration for sample processing
	Pulse-chase experiment for RNA half-life analysis
	scNT-Seq library preparation and sequencing
	SLAM-Seq reaction on barcoded beads
	Second-strand synthesis on barcoded beads
	Read alignment and quantification of metabolically labeled transcripts
	Cell-type clustering and dataset integration
	Estimation of the fraction of newly-synthesized transcripts
		Computing aggregated new transcripts for each cell type or state
		Computing new transcripts in single cells
	Gene ontology enrichment analysis
	Identification of differentially expressed genes (DEGs)
	Estimation of RNA half-life
	Splicing kinetics-based RNA velocity analysis
	Metabolic labeling-based RNA velocity analysis
	Estimation of RNA biogenesis rate and degradation rate constant
	Analysis of single-cell regulon activity using new and old RNAs
	Data visualization
	Statistics.
	Reporting Summary.
	Data availability
	Code availability
Extended Data
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References
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