Single-cell RNA-seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis

By J. Shin, Daniel A. Berg, Yunhua Zhu, Joseph Y. Shin, Juan Song, M. Bonaguidi, G. Enikolopov, David W. Nauen, Kimberly M. Christian, Guo-li Ming and 1 more. Cell Stem Cell, 2015

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SUMMARY Somatic stem cells contribute to tissue ontogenesis, homeostasis, and regeneration through sequential processes. Systematic molecular analysis of stem cell behavior is challenging because classic approaches cannot resolve cellular heterogeneity or capture developmental dynamics. Here we provide a comprehensive resource of single-cell transcriptomes of adult hippocampal quiescent neural stem cells (qNSCs) and their immediate progeny. We further developed Waterfall, a bioinformatic suite, to statistically quantify singe-cell gene expression along de novo reconstructed continuous developmental trajectory. Our study reveals molecular signatures of adult qNSCs, characterized by active niche signaling integration and low protein translation capacity. Our analyses further delineate molecular cascades underlying qNSC activation and neurogenesis initiation, exemplified by decreased extrinsic signaling capacity, primed translational machinery, and regulatory switches in transcription factors, metabolism, and energy sources. Our study reveals the molecular continuum underlying adult neurogenesis and illustrates how Waterfall can be used for single-cell omics analyses of various continuous biological processes.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b0851be5a5b83735cab4aeefbf9f64d0f836fc88

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Citations in the corpus

Human haematopoietic stem cell lineage commitment is a continuous process (2017, positive): “Here we developed an approach that integrates the reconstruction of developmental trajectories 18,19 with the quantitative linkage between transcriptomic and functional single cell data 17 and thus provides a detailed view on lineage commitment of individual haematopoietic stem and progenitor cells…”
Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “…expressed between time points or, if data at multiple time points are not available, choosing genes above a mean and variance threshold Waterfall [80] Unsupervised clustering is used to identify clusters of cells for which a putative ordering is determined on the basis of their relative location…”
The Human Cell Atlas (2017, background): “…(Bendall et al., 2014), and from single-cell RNA expression during myogenesis in vitro (Trapnell et al., 2014), early hematopoiesis (Nestorowa et al., 2016), neurogenesis in vivo (Habib et al., 2016; Shin et al., 2015), and reprogramming from fibroblasts to neurons (Treutlein et al., 2016).”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “For example, cellular differentiation processes are strongly linked to the cell cycle 101,142,157,158 , yet some of their aspects may not be apparent in a cell’s molecular profile due to the cell cycle’s large effect 104 .”
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics (2017, methodology): “In comparison, using an MST alone to identify lineages and estimate pseudotimes (as is done by Waterfall and TSCAN) gives estimates that are quite sensitive to the choice of clusters.”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “A number of tools have been proposed for trajectory inference, e.g., Monocle (Trapnell et al., 2014), Waterfall (Shin et al., 2015), Wishbone (Setty et al., 2016), TSCAN (Ji and Ji, 2016), Monocle2 (Qiu et al., 2017), Slingshot (Street et al., 2018), and CellRouter (Lummertz da Rocha et al., 2018)…”
Single-cell transcriptome sequencing: recent advances and remaining challenges (2016, background): “More recently, Shin et al. 92 and Moignard et al. 93 have outlined additional strategies for reconstructing cell trajectories.”
Adult Neurogenesis in the Hippocampus: From Stem Cells to Behavior. (2016, neutral): “Stem cells upregulate genes for oxidative phosphorylation at the time they become activated and enter proliferation (Shin et al., 2015).”
Wishbone identifies bifurcating developmental trajectories from single-cell data (2016, background): “Recently, several reports have demonstrated approaches to order single cells based on their maturity [3, 4].”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “…, for example, implements bifurcation analysis and has been used to recover lineages during early development in mouse embryos from gene expression pro ﬁ les at multiple time-point measurements. scRNA-seq has also been successfully applied to reconstruct lineages during in vivo neurogenesis 90,91 .”
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis (2016, methodology): “Although the cell clustering idea has also been used previously in Waterfall, a systematic evaluation of the impact of cell clustering on cell ordering was not provided in the Waterfall study (26).”