Special nuclear layer contacts between starburst amacrine cells in the mouse retina

Front Ophthalmol (Lausanne). 2023 Mar 24:3:1129463. doi: 10.3389/fopht.2023.1129463. eCollection 2023.

Abstract

Starburst amacrine cells are a prominent neuron type in the mammalian retina that has been well-studied for its role in direction-selective information processing. One specific property of these cells is that their dendrites tightly stratify at specific depths within the inner plexiform layer (IPL), which, together with their unique expression of choline acetyltransferase (ChAT), has made them the most common depth marker for studying other retinal neurons in the IPL. This stratifying property makes it unexpected that they could routinely have dendrites reaching into the nuclear layer or that they could have somatic contact specializations, which is exactly what we have found in this study. Specifically, an electron microscopic image volume of sufficient size from a mouse retina provided us with the opportunity to anatomically observe both microscopic details and collective patterns, and our detailed cell reconstructions revealed interesting cell-cell contacts between starburst amacrine neurons. The contact characteristics differ between the respective On and Off starburst amacrine subpopulations, but both occur within the soma layers, as opposed to their regular contact laminae within the inner plexiform layer.

Keywords: 3D reconstruction; electron microscopy; perisomatic contact; retina; starburst amacrine cells (SACs).

Grants and funding

This research was supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior/Interior Business Center (DoI/IBC) contract number D16PC0005, NIH/NCI (5UH2CA203710), NIH/NIMH (RF1MH117815, RF1MH123400), NIH/NINDS (5U01NS090562, U01NS09054, 5R01NS104926). The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/IBC, or the U.S. Government. We are grateful for assistance from Google, Amazon, and Intel.