Single-Cell RNA-Seq with Waterfall Reveals Molecular Cascades underlying Adult Neurogenesis
Open accessAbstract
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 pipeline, to statistically quantify singe-cell gene expression along a 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.
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Emerging in situ single-cell RNA methods have not yet matched established protocols in throughput and accessibility.
“While pioneering approaches for in situ sequencing154,155, transcriptome in vivo analysis156, and multiplex FISH157–159 of single-cell RNA are emerging (see also a recent review160), their throughput and accessibility to the research community have not yet matched those of more established protocols.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [157]
Single cell transcriptome analysis enables the reconstruction of gene expression dynamics along differentiation trajectories.
“Single cell transcriptome analysis enables the reconstruction of gene expression dynamics along differentiation trajectories (Setty et al., 2016; Shin et al., 2015; Trapnell et al., 2014).”
From Single cell transcriptomics reveals spatial and temporal dynamics of gene expression in the developing mouse spinal cord (2019) · cites this paper as Shin et al., 2015
Existing computational methods for predicting lineage trajectories generally rely on the presence of intermediate cell states to reconstruct the trajectory.
“and (ii) the presence of intermediate cell states to reconstruct the trajectory (8, 9).”
From Single-cell transcriptional diversity is a hallmark of developmental potential (2020) · cites this paper as [9]
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Alignment of single-cell trajectories to compare cellular expression dynamics
Alpert, A., Moore, L. S., Dubovik, T., & Shen-Orr, S. S. (2018) · Nature Methods · doi:10.1038/nmeth.4628 · Cited by 96 · see citing works
Differentiation Drives Widespread Rewiring of the Neural Stem Cell Chaperone Network
Vonk, W. I. M., Rainbolt, T. K., Dolan, P. T., Webb, A. E., Brunet, A., & Frydman, J. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.03.009 · Cited by 84 · see citing works
Exploring single cells in space and time during tissue development, homeostasis and regeneration
Mayr, U., Serra, D., & Liberali, P. (2019) · Development · doi:10.1242/dev.176727 · Cited by 63 · see citing works
Synergic functions of miRNAs determine neuronal fate of adult neural stem cells
Pons-Espinal, M., de Luca, E., Marzi, M. J., Beckervordersandforth, R., Armirotti, A., Nicassio, F., Fabel, K., Kempermann, G., & De Pietri Tonelli, D. (2017) · Stem Cell Reports · doi:10.1016/j.stemcr.2017.02.012 · Cited by 59 · see citing works
Overall, R. W., Walker, T. L., Fischer, T. J., Brandt, M. D., & Kempermann, G. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00362 · Cited by 48 · see citing works
Single-Cell Computational Strategies for Lineage Reconstruction in Tissue Systems
Herring, C. A., Chen, B., McKinley, E. T., & Lau, K. S. (2018) · Cellular and Molecular Gastroenterology and Hepatology · doi:10.1016/j.jcmgh.2018.01.023 · Cited by 35 · see citing works
Maybury‐Lewis, S. Y., Brown, A. K., Yeary, M., Sloutskin, A., Dhakal, S., Juven‐Gershon, T., & Webb, A. E. (2021) · Aging Cell · doi:10.1111/acel.13499 · Cited by 27 · see citing works
Fundamental limits on dynamic inference from single cell snapshots
Weinreb, C., Wolock, S., Tusi, B. K., Socolovsky, M., & Klein, A. M. (2017) · doi:10.1101/170118 · Cited by 27 · see citing works
Vascular Senescence: A Potential Bridge Between Physiological Aging and Neurogenic Decline
Rojas-Vázquez, S., Blasco-Chamarro, L., López-Fabuel, I., Martínez-Máñez, R., & Fariñas, I. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.666881 · Cited by 15 · see citing works
- Authors
- Shin, J., Berg, D. A., Zhu, Y., Shin, J. Y., Song, J., Bonaguidi, M. A., Enikolopov, G., Nauen, D. W., Christian, K. M., Ming, G.-L., & Song, H.
- Affiliations
- Johns Hopkins University (US)
- Cold Spring Harbor Laboratory (US)
- Corresponding author
- Song, Hongjun
- ORCID
- Venue
- Cell Stem Cell
- Publisher
- Elsevier BV
- Published
- 2015-09
- Volume
- 17
- Issue
- 3
- Pages
- 360-372
- ISSN
- 1875-9777 (ISSN-L)
- 1934-5909
- PMC
- PMC8638014
- OpenAlex
- W1874466506
- Cited by
- 784
- References
- 37
- Citations by year
- 2026: 21 · 2025: 49 · 2024: 64 · 2023: 68 · 2022: 80 · 2021: 76 · 2020: 79 · 2019: 88 · 2018: 101 · 2017: 87 · 2016: 62 · 2015: 6 · 2013: 1 · 2012: 1
- FWCI
- 36.97
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cell stem cell (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Cold Spring Harbor Laboratory Institutional Repository (Cold Spring Harbor Laboratory) (submittedVersion)
- Funders
- National Institute of Mental Health (R01MH105128)
- National Institute of Neurological Disorders and Stroke (K99NS080913, P01NS097206, R37NS047344)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Neuroscience › Developmental Neuroscience › Neurogenesis and neuroplasticity mechanisms
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Developmental Biology and Gene Regulation
- Keywords
- adult neurogenesis, quiescent neural stem cells, hippocampus, single-cell transcriptomics, stem cell activation, niche signaling, translation machinery, transcription factors, energy metabolism, cellular heterogeneity
- MeSH terms
- Aging, Animals, Mice, Transgenic, Models, Biological, Transcription Factors, Reproducibility of Results, Down-Regulation, Up-Regulation, Sequence Analysis, RNA, Dentate Gyrus, Databases as Topic, Gene Expression Profiling, Neurogenesis, Molecular Dynamics Simulation, Neural Stem Cells, Single-Cell Analysis, Transcriptome
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