(193ah) Comprehensive Molecular Classification of Cell Types and Cell-Type Specific Response to Tissue Injury Using Massively Parallel Single-Cell Genomics

Neurons of the central nervous system (CNS) have been historically categorized into discrete "types" based on structure, physiological responses, connectivity patterns, and molecular profiles. Even in the smallest of brain regions, the number of resident cell types are estimated to be upto a hundred, or even more. Cellular heterogeneity in the nervous system, and in other tissues, can have immense functional consequences- e.g. recent studies have found that some neuronal types in the retina are more resilient than other types to optic nerve injury, an event that leads to irrecoverable damage in vision. Exploiting such heterogeneity for therapeutic ends requires that the cell types, which constitute the "parts list" of a tissue, be identified and molecularly characterized.

Our work focuses on the central nervous system, and the retina in particular, a tissue that communicates visual signals to the brain. It is as complex as any other region of the brain (containing ~120 neuronal types that are largely unmapped molecularly), but benefits from having a compact, accessible structure, and experimental tools make it especially suited for detailed analyses. By cutting-edge microfluidic technologies to measure gene expression in single-cells, advanced computational approaches rooted in machine learning, and molecular and genetic tools, we will describe our efforts to define the molecular identity of neuronal types in the mouse retina comprehensively, and to connect molecular definitions in vivo. Using this as a foundational tool, this work aims to explore the functional consequences of this heterogeneity during nerve injury. Specifically, we aim to explore the selective resilience of certain cell types to optic nerve injury, and identify factors that underlie resilience. I will specifically describe our earlier and ongoing efforts to,

1) Complete the census of the mouse retina, which will the first for any brain region, by inferring molecular taxonomies of its main cellular classes (photoreceptors, bipolar cells, amacrine cells, retinal ganglion cells) from data collected using high-throughput single-cell RNA-sequencing in droplets. This work has already led to the discovery of new cell types in this tissue (Shekhar et al., Cell, 2016), and generated a molecular database of genetic markers that is already being used by biologists for directed studies. Furthermore, the computational approaches developed to infer molecular identities of cells are being adopted to analyze other tissues (Villani and Shekhar, Methods in Molecular Biology, 2017).

2) Conduct a systematic investigation of cell-type specific responses in the retina to optic nerve injury. This usually leads to a rapid, stereotypic death of retinal ganglion cells (RGCs), but a recent study from our lab reported that some RGC types are more resilient than others. Since the different RGC types are intermingled in the tissue, and experience the same extracellular mileu, differences in their survival and regenerative capacities are likely to reflect intrinsic properties. Using 1) as a foundational resource, we have found that two classes of RGCs (alpha-RGCs and intrinsically photosensitive RGCs) selectively survive nerve injury, while some others (e.g. direction selective RGCs) are especially susceptible. We are currently investigating cell intrinsic and non-automous factors, that make these RGC types resilient. Such selective cell type resilience is increasingly recognized as a feature of diseases like glaucoma and stroke. This work will identify principles to guide therapies that need to cater to the specific requirements of different cell types.