Unified Classification of Mouse Retinal Ganglion Cells Using Function, Morphology, and Gene Expression

Goetz, Jillian J.; Jessen, Zachary F.; Jacobi, Anne; Mani, Adam; Cooler, Sam; Greer, Devon; Kadri, Sabah; Segal, Jeremy P.; Shekhar, Karthik; Sanes, Joshua R.; Schwartz, Gregory William

doi:10.2139/ssrn.3867730

Cited by 4 publications

(9 citation statements)

References 76 publications

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“…For RGCs, class identity has been clear for a century, but inventories of subclasses and types have emerged only over the last few decades as high-throughput methods have been implemented for quantifying structural (primarily dendritic morphology), functional (responses to an array of visual stimuli), and molecular properties (gene and transgene expression) of large numbers of RGCs. Fortunately, to the extent that they have been compared, there is excellent concordance among types defined by molecular, structural, and physiological criteria ( Bae et al, 2018 ; Goetz et al, 2021 ; Tran et al, 2019 ; see http://www.rgc.types.org ). Moreover, RGCs of a single type exhibit a regular spacing, called a mosaic arrangement, in that they tend to avoid other members of the same cell type, whereas their association with members of other types is random ( Kay et al, 2012 ; Keeley et al, 2020 ; Rockhill et al, 2000 ).…”

Section: Discussionmentioning

confidence: 99%

“…Second, RGCs have now been classified by functional and structural properties, based on physiological responses to visual stimuli ( Baden et al, 2016 ; Goetz et al, 2021 ) and serial section electron microscopy ( Bae et al, 2018 ). The numbers of types defined in these ways (47 in Bae et al, 2018 , 42 in Goetz et al, 2021 , and >32 in Baden et al, 2016 ) match well to the 45–46 defined molecularly ( Tran et al, 2019 ).…”

Section: Discussionmentioning

confidence: 99%

“…The retina contains five classes of neurons – photoreceptors that sense light, three interneuronal classes (horizontal, bipolar, and amacrine cells) that process visual information, and retinal ganglion cells (RGCs) that pass the information to the rest of the brain through the optic nerve ( Figure 1a ; Masland, 2012 ). These classes can be divided into numerous types, ~130 in mouse and chick, each of which has characteristic morphological, physiological, and molecular properties, and plays distinct roles in information processing ( Baden et al, 2016 ; Franke et al, 2017 ; Goetz et al, 2021 ; Macosko et al, 2015 ; Rheaume et al, 2018 ; Shekhar et al, 2016 ; Shekhar and Sanes, 2021 ; Tran et al, 2019 ; Yamagata et al, 2021 ; Yan et al, 2020 ). Remarkably, nearly all types are distributed across the entire retina ( Kay et al, 2012 ; Keeley et al, 2020 ; Rockhill et al, 2000 ), so morphogen gradients, which play a critical role in other parts of the central nervous system (e.g., Sagner and Briscoe, 2019 ), cannot provide an explanation for retinal neuronal diversification ( Marquardt and Gruss, 2002 ).…”

Section: Introductionmentioning

confidence: 99%

“…All RGCs are similar in many respects: for example, they all elaborate dendrites that receive input from amacrine and bipolar interneurons, send axons through the optic nerve, and use glutamate as a neurotransmitter ( Sanes and Masland, 2015 ). However, they differ in molecular, morphological, and physiological details, which have led to their division into ~45 distinct types in mice ( Baden et al, 2016 ; Bae et al, 2018 ; Goetz et al, 2021 ; Rheaume et al, 2018 ; Tran et al, 2019 ). Most of these types appear to be feature detectors that collectively transmit a diverse set of highly processed images of the visual world to the rest of the brain ( Baden et al, 2020 ; Sanes and Masland, 2015 ).…”

Section: Introductionmentioning

confidence: 99%

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

Shekhar

Whitney

Butrus

et al. 2022

eLife

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Shekhar

Whitney

Butrus

et al. 2022

eLife

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“…We showed recently that 45 molecularly-defined mouse RGC types, many of which map to physiologically and morphologically-defined mouse RGC types (Goetz et al, 2021), can be grouped into subsets defined by selectively expressed TFs (Shekhar et al, 2022;Tran et al, 2019;Whitney et al, 2023). Some of these TFs (e.g.…”

Section: Retinal Ganglion Cell Orthotypesmentioning

confidence: 99%

Evolution of neuronal cell classes and types in the vertebrate retina

Hahn

Monavarfeshani

Qiao

et al. 2023

Preprint

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The basic plan of the retina is conserved across vertebrates, yet species differ profoundly in their visual needs. One might expect that retinal cell types evolved to accommodate these varied needs, but this has not been systematically studied. Here, we generated and integrated single-cell transcriptomic atlases of the retina from 17 species: humans, two non-human primates, four rodents, three ungulates, opossum, ferret, tree shrew, a teleost fish, a bird, a reptile and a lamprey. Molecular conservation of the six retinal cell classes (photoreceptors, horizontal cells, bipolar cells, amacrine cells, retinal ganglion cells [RGCs] and Müller glia) is striking, with transcriptomic differences across species correlated with evolutionary distance. Major subclasses are also conserved, whereas variation among types within classes or subclasses is more pronounced. However, an integrative analysis revealed that numerous types are shared across species based on conserved gene expression programs that likely trace back to the common ancestor of jawed vertebrates. The degree of variation among types increases from the outer retina (photoreceptors) to the inner retina (RGCs), suggesting that evolution acts preferentially to shape the retinal output. Finally, we identified mammalian orthologs of midget RGCs, which comprise >80% of RGCs in the human retina, subserve high-acuity vision, and were believed to be primate-specific; in contrast, the mouse orthologs comprise <2% of mouse RGCs. Projections both primate and mouse orthologous types are overrepresented in the thalamus, which supplies the primary visual cortex. We suggest that midget RGCs are not primate innovations, but descendants of evolutionarily ancient types that decreased in size and increased in number as primates evolved, thereby facilitating high visual acuity and increased cortical processing of visual information.

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Differences in nonlinearities determine retinal cell types

Trapani

Spampinato

Yger

et al. 2023

Journal of Neurophysiology

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Classifying neurons in different types is still an open challenge. In the retina, recent works have taken advantage of the ability to record a large number of cells to classify ganglion cells into different types based on functional information. While the first attempts in this direction used the receptive field properties of each cell to classify them, more recent approaches have proposed to cluster ganglion cells directly based on their response to standard stimuli. These two approaches have not been compared directly. Here we recorded the responses of a large number of ganglion cells and compared two methods for classifying them into types, one based on the receptive field properties, and the other one using their responses to standard stimuli. We show that the stimulus-based approach allows separating more types than the receptive field-based method, leading to a better classification. This better granularity is due to the fact that the stimulus-based method takes into account not only the linear part of ganglion cell function, but also non-linearities. A careful characterization of non-linear processing is thus key to allow functional classification of sensory neurons.

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Unified Classification of Mouse Retinal Ganglion Cells Using Function, Morphology, and Gene Expression

Cited by 4 publications

References 76 publications

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

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

Evolution of neuronal cell classes and types in the vertebrate retina

Differences in nonlinearities determine retinal cell types

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