Defining Selective Neuronal Resilience and Identifying Targets of Neuroprotection and Axon Regeneration Using Single-Cell RNA Sequencing: Computational Approaches

Salwan Butrus; Srikant Sagireddy; Wenjun Yan; Karthik Shekhar

doi:10.1007/978-1-0716-3012-9_2

Defining Selective Neuronal Resilience and Identifying Targets of Neuroprotection and Axon Regeneration Using Single-Cell RNA Sequencing: Computational Approaches

Methods Mol Biol. 2023:2636:19-41. doi: 10.1007/978-1-0716-3012-9_2.

Authors

Salwan Butrus¹, Srikant Sagireddy², Wenjun Yan³, Karthik Shekhar^{4

5}

Affiliations

¹ Department of Chemical and Biomolecular Engineering, UC Berkeley, Berkeley, CA, USA. salwan@berkeley.edu.
² Department of Chemical and Biomolecular Engineering, UC Berkeley, Berkeley, CA, USA.
³ Center for Brain Science and Department of Molecular and Cell Biology, Harvard University, Cambridge, MA, USA.
⁴ Department of Chemical and Biomolecular Engineering, UC Berkeley, Berkeley, CA, USA. kshekhar@berkeley.edu.
⁵ Helen Wills Neuroscience Institute, California Institute for Quantitative Biosciences, QB3, Center for Computational Biology, and Biophysics Graduate Program UC Berkeley, Berkeley, CA, USA. kshekhar@berkeley.edu.

PMID: 36881293
DOI: 10.1007/978-1-0716-3012-9_2

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.

Keywords: Machine learning; Optic nerve crush; Retinal ganglion cells; Single-cell RNA-sequencing; Supervised classification.

Publication types

Research Support, U.S. Gov't, Non-P.H.S.
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Animals
Axons*
Mice
Nerve Regeneration / genetics
Neuroprotection*
Retinal Ganglion Cells
Sequence Analysis, RNA

Grants and funding

R00 EY028625/EY/NEI NIH HHS/United States