Abstract
We describe a computational workflow to analyze single-cell RNA-sequencing (scRNA-seq) profiles of axotomized retinal ganglion cells (RGCs) in mice. Our goal is to identify differences in the dynamics of survival among 46 molecularly defined RGC types together with molecular signatures that correlate with these differences. The data consists of scRNA-seq profiles of RGCs collected at six time points following optic nerve crush (ONC) (see companion chapter by Jacobi and Tran). We use a supervised classification-based approach to map injured RGCs to type identities and quantify type-specific differences in survival at 2 weeks post crush. As injury-related changes in gene expression confound the inference of type identity in surviving cells, the approach deconvolves type-specific gene signatures from injury responses by using an iterative strategy that leverages measurements along the time course. We use these classifications to compare expression differences between resilient and susceptible subpopulations, identifying potential mediators of resilience. The conceptual framework underlying the method is sufficiently general for analysis of selective vulnerability in other neuronal systems.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Tran NM, Shekhar K, Whitney IE et al (2019) Single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes neuroresource single-cell profiles of retinal ganglion cells differing in resilience to injury reveal neuroprotective genes. Neuron 104:1039–1055. https://doi.org/10.1016/j.neuron.2019.11.006
Williams PR, Benowitz LI, Goldberg et al (2020) Axon regeneration in the mammalian optic nerve. Ann Rev Vis Sci 6:195–213. https://doi.org/10.1146/annurev-vision-022720-094953
Sanes JR, Masland RH (2015) The types of retinal ganglion cells: current status and implications for neuronal classification. Annu Rev Neurosci 38:221–246. https://doi.org/10.1146/annurev-neuro-071714-034120
Zheng GXY, Terry JM, Belgrader P et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8. https://doi.org/10.1038/ncomms14049
Baden T, Berens P, Franke K et al (2016) The functional diversity of retinal ganglion cells in the mouse. Nature 529:345–350. https://doi.org/10.1038/nature16468
Bae JA, Mu S, Kim JS et al (2018) Digital museum of retinal ganglion cells with dense anatomy and physiology. Cell 173:1293–1306.e19. https://doi.org/10.1016/j.cell.2018.04.040
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proc ACM SIGKDD Int Conf Knowl Discov Data Min, 13–17 August 2016, pp 785–794. https://doi.org/10.1145/2939672.2939785
Wolf F, Angerer P, Theis F (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19:15. https://doi.org/10.1186/s13059-017-1382-0
Korsunsky I, Millard N, Fan J et al (2019) Fast, sensitive and accurate integration of single-cell data with harmony. Nat Methods 16:1289–1296. https://doi.org/10.1038/s41592-019-0619-0
The HDF Group (2000–2010) Hierarchical data format version 5. http://www.hdfgroup.org/HDF5
Pandey S, Shekhar K, Regev A, Schier AF (2018) Comprehensive identification and spatial mapping of habenular neuronal types using single-cell RNA-Seq. Curr Biol 28:1052–1065.e7. https://doi.org/10.1016/j.cub.2018.02.040
Hotelling H (1933) Analysis of a complex of statistical variables into principle components. J Educ Psychol 24:417–441, 498–520
McInnes L, Healy J, Melville J (2018) UMAP: uniform manifold approximation and projection for dimension reduction. arXiv
Acknowledgments
S.B. would like to acknowledge support from the NSF Graduate Research Fellowship Program (grant DGE 1752814). K. S. acknowledges the support of the National Institutes of Health (grant R00EY028625), Glaucoma Research Foundation (CFC4), and UC Berkeley. We would like to gratefully acknowledge critical feedback from Drs. Anne Jacobi and Nicholas Tran.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature
About this protocol
Cite this protocol
Butrus, S., Sagireddy, S., Yan, W., Shekhar, K. (2023). Defining Selective Neuronal Resilience and Identifying Targets of Neuroprotection and Axon Regeneration Using Single-Cell RNA Sequencing: Computational Approaches. In: Udvadia, A.J., Antczak, J.B. (eds) Axon Regeneration. Methods in Molecular Biology, vol 2636. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-3012-9_2
Download citation
DOI: https://doi.org/10.1007/978-1-0716-3012-9_2
Published:
Publisher Name: Humana, New York, NY
Print ISBN: 978-1-0716-3011-2
Online ISBN: 978-1-0716-3012-9
eBook Packages: Springer Protocols