Title: Development and evolution of neural diversity in the visual system: a molecular perspective.
The brain’s computational power derives from the diversity and complexity of its building blocks. In tour de force experiments conducted over a century ago, Ramon y Cajal deduced that neurons, the cells of the brain, can be classified into several discrete types based on morphological attributes. Cajal’s prediction was borne out by studies in the 20th century, which showed that morphologically diverse neurons have an equally impressive breadth of functional and molecular characteristics. Despite its foundational importance, however, the enterprise of classifying neuronal types remained incomplete, ad hoc and non-quantitative until very recently.
I will begin by describing our early efforts to deploy a new set of tools rooted in high-throughput single-cell genomic technologies to survey and classify neural diversity in complex tissues based on molecular fingerprints of individual neurons. Given the large-scale and noisy nature of these measurements, there have also been rapid developments in computational approaches to analyze single-cell genomic datasets and glean biological insights. Over the past few years, through a close collaboration between experimental measurements and computational analyses, our efforts in this space have led to a near complete census of neural diversity in the retina, the neural tissue where vision begins (Shekhar and Sanes, Annual Rev. Vision. Sci., 2021). I will describe evidence that the molecular fingerprint of a neuron is a robust proxy for its morphological and functional identity, but one that also enables specific subsets of neurons to be experimentally marked and manipulated through gene-targeting approaches.
Given this background, I will next describe recent work in my group that seeks to understand how neural diversity arises within an organism during early development (“Ontogeny”) and how neural diversity arises during evolution (“Phylogeny”). In both pursuits, we leverage the molecular state of a neuron (mRNA and chromatin) to trace its trajectory during development, and to establish evolutionary correspondences across species. Because measuring the molecular state of a neuron invariably destroys it, understanding the development and evolution of neural diversity requires statistical inference methods applied to genomic datasets. We have recently applied this approach to trace the early diversification and maturation of 45 distinct types of retinal ganglion cells in mice from immature precursors, and identify genes that are likely the key players in fate determination (Shekhar et al., eLife, 2022). In ongoing work, we are analyzing the conservation of retinal neuronal types in 18 vertebrate species to build a quantitative understanding of how neuronal types are lost, gained or modified to suit the retina of each species to its visual needs. An exciting preliminary finding is that some neuronal types thought to be unique to primates (and therefore difficult to study and clinically important) may indeed have clear counterparts in many other species, including mice.
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