Diversification of multipotential postmitotic mouse retinal ganglion cell precursors into discrete types

Abstract

The genesis of broad neuronal classes from multipotential neural progenitor cells has been extensively studied, but less is known about the diversification of a single neuronal class into multiple types. We used single-cell RNA-seq to study how newly-born (postmitotic) mouse retinal ganglion cell (RGC) precursors diversify into ~45 discrete types. Computational analysis provides evidence that RGC transcriptomic type identity is not specified at mitotic exit, but acquired by gradual, asynchronous restriction of postmitotic multipotential precursors. Some types are not identifiable until a week after they are generated. Immature RGCs may be specified to project ipsilaterally or contralaterally to the rest of the brain before their type identity emerges. Optimal transport inference identifies groups of RGC precursors with largely non-overlapping fates, distinguished by selectively expressed transcription factors that could act as fate determinants. Our study provides a framework for investigating the molecular diversification of discrete types within a neuronal class.

Vision-dependent and -independent molecular maturation of mouse retinal ganglion cells

“…Finally, diversification may in many cases occur in two steps, with precursors initially committing to subclasses, each defined by the selective expression of TFs in the adult. Subsequently, precursors within a subclass become restricted to single types by a process we named “fate-decoupling” (Shekhar et al , 2022).…”

“…Gene Expression Matrices (GEMs) for normally reared (NR) RGCs for E13, E14, E16, P0, P5 and P56 were downloaded from our previous studies (Shekhar et al , 2022; Tran et al , 2019), Entries of raw GEMs reflect the number of unique molecular identifiers (nUMIs) detected per gene per cell, which is a proxy for transcript copy numbers. Raw GEMs were filtered, normalized and log-transformed following standard procedures described before (Shekhar et al , 2022).…”

“…For each VD condition, RGCs from two separate groups of mice were profiled. Identical protocols were used to isolate RGCs from dark-reared (DR), rd1 and Vsx2 -SE -/- mice at P56 and profiled using the same methods described previously for normally reared mice (Shekhar et al , 2022; Tran et al , 2019), with one exception. Retinas from DR mice were dissected in a dark room using a microscope fitted with night-vision binocular goggles (Tactical Series G1, Night Owl), and an external infrared light source, with dissociation and staining steps conducted in LiteSafe tubes (Argos Technologies) to protect from light exposure.…”

“…Gene Expression Matrices (GEMs) for normally reared (NR) RGCs for E13, E14, E16, P0, P5 and P56 were downloaded from our previous studies (Shekhar et al , 2022; Tran et al , 2019), Entries of raw GEMs reflect the number of unique molecular identifiers (nUMIs) detected per gene per cell, which is a proxy for transcript copy numbers. Raw GEMs were filtered, normalized and log-transformed following standard procedures described before (Shekhar et al , 2022). For each RGC in this dataset, we retained metadata corresponding to cluster/type IDs and also fate associations computed using WOT in (Shekhar et al , 2022), which allowed us to identify putative type-specific precursors at each age.…”

“…First, we analyzed the transcriptomes of 110,814 single RGCs at six ages from embryonic day (E)13, when RGCs are newly postmitotic, to adulthood (postnatal day [P]56; mice are sexually mature by around P40). We previously used these datasets to define 45 mouse RGC types (Tran et al , 2019), and analyze their diversification from a limited repertoire of postmitotic precursors (Shekhar et al, 2022). Here, we surveyed shared and type-specific gene expression changes to identify candidate regulators of RGC development.…”