Diversification of multipotential postmitotic mouse retinal ganglion cell precursors into discrete types DOI Creative Commons
Karthik Shekhar,

Irene E. Whitney,

Salwan Butrus

et al.

eLife, Journal Year: 2022, Volume and Issue: 11

Published: Feb. 22, 2022

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

Language: Английский

Single-Cell Profiles of Retinal Ganglion Cells Differing in Resilience to Injury Reveal Neuroprotective Genes DOI Creative Commons
Nicholas M. Tran, Karthik Shekhar,

Irene E. Whitney

et al.

Neuron, Journal Year: 2019, Volume and Issue: 104(6), P. 1039 - 1055.e12

Published: Nov. 26, 2019

Language: Английский

Citations

535
Tissue clearing and its applications in neuroscience DOI Open Access
Hiroki R. Ueda, Ali Ertürk,

Kwanghun Chung

et al.

Nature reviews. Neuroscience, Journal Year: 2020, Volume and Issue: 21(2), P. 61 - 79

Published: Jan. 2, 2020

Language: Английский

Citations

504
Molecular Classification and Comparative Taxonomics of Foveal and Peripheral Cells in Primate Retina DOI Creative Commons
Yi‐Rong Peng, Karthik Shekhar, Wenjun Yan

et al.

Cell, Journal Year: 2019, Volume and Issue: 176(5), P. 1222 - 1237.e22

Published: Jan. 31, 2019

Language: Английский

Citations

445
Single cell transcriptome profiling of retinal ganglion cells identifies cellular subtypes DOI Creative Commons
Bruce A. Rheaume,

Amyeo Jereen,

Mohan Bolisetty

et al.

Nature Communications, Journal Year: 2018, Volume and Issue: 9(1)

Published: July 11, 2018

Retinal ganglion cells (RGCs) convey the major output of information collected from eye to brain. Thirty subtypes RGCs have been identified date. Here, we analyze 6225 (average 5000 genes per cell) right and left eyes by single-cell RNA-seq classify them into 40 using clustering algorithms. We identify additional markers, as well transcription factors predicted cooperate in specifying RGC subtypes. Zic1, a marker eye-enriched subtype, is validated immunostaining situ. Runx1 Fst, markers other subtypes, are purified fluorescent situ hybridization (FISH) immunostaining. show extent gene expression variability needed for subtype segregation, hierarchy diversification cell-type population Finally, present website comparing

Language: Английский

Citations

394
Glia-to-Neuron Conversion by CRISPR-CasRx Alleviates Symptoms of Neurological Disease in Mice DOI Creative Commons
Haibo Zhou, Jinlin Su,

Xinde Hu

et al.

Cell, Journal Year: 2020, Volume and Issue: 181(3), P. 590 - 603.e16

Published: April 1, 2020

Language: Английский

Citations

386
Digital Museum of Retinal Ganglion Cells with Dense Anatomy and Physiology DOI Creative Commons
J. Alexander Bae, Shang Mu, Jinseop S. Kim

et al.

Cell, Journal Year: 2018, Volume and Issue: 173(5), P. 1293 - 1306.e19

Published: May 1, 2018

Language: Английский

Citations

248
Diverse Central Projection Patterns of Retinal Ganglion Cells DOI Creative Commons

Emily M. Martersteck,

Karla E. Hirokawa,

Mariah Evarts

et al.

Cell Reports, Journal Year: 2017, Volume and Issue: 18(8), P. 2058 - 2072

Published: Feb. 1, 2017

Highlights•Anatomical characterization of Cre expression in the retina 88 driver lines•Morphological and histochemical classification Cre+ RGC types 26 lines•High resolution whole brain imaging labeled retinal axons reveals central targets•Correspondences described between line projection patternsSummaryUnderstanding how >30 ganglion cells (RGCs) mouse each contribute to visual processing will require more tools that label manipulate specific RGCs. We screened analyzed recombinase using transgenic lines. In many lines, was expressed multiple cell classes, but several exhibited selective expression. comprehensively mapped projections from RGCs lines viral tracers, high-throughput imaging, a data pipeline. identified over 50 retinorecipient regions present quantitative retina-to-brain connectivity map, enabling comparisons target-specificity across Projections two major targets were notably correlated: projecting outer shell or core lateral geniculate projected superficial deep layers within superior colliculus, respectively. Retinal images are available online at http://connectivity.brain-map.org.Graphical abstract

Language: Английский

Citations

247
Four alpha ganglion cell types in mouse retina: Function, structure, and molecular signatures DOI Creative Commons

Brenna Marie Krieger,

Mu Qiao,

D. Rousso

et al.

PLoS ONE, Journal Year: 2017, Volume and Issue: 12(7), P. e0180091 - e0180091

Published: July 28, 2017

The retina communicates with the brain using ≥30 parallel channels, each carried by axons of distinct types retinal ganglion cells. In every mammalian one finds so-called "alpha" cells (αRGCs), identified their large cell bodies, stout axons, wide and mono-stratified dendritic fields, high levels neurofilament protein. mouse, three αRGC have been described based on responses to light steps: On-sustained, Off-sustained, Off-transient. Here we employed a transgenic mouse line that labels αRGCs in live retina, allowing systematic targeted recordings. We characterize known identify fourth, On-transient responses. All four share basic aspects visual signaling, including receptive field center, weak antagonistic surround, absence any direction selectivity. They also distinctive waveform action potential, faster than other RGC types. Morphologically, they differ level stratification within IPL, which accounts for response properties. Molecularly, type has signature. A comparison across mammals suggests common theme, large-bodied split signal into channels arranged symmetrically respect polarity kinetics.

Language: Английский

Citations

231
Cell Atlas of The Human Fovea and Peripheral Retina DOI Creative Commons
Wenjun Yan, Yi‐Rong Peng, Tavé van Zyl

et al.

Scientific Reports, Journal Year: 2020, Volume and Issue: 10(1)

Published: June 17, 2020

Abstract Most irreversible blindness results from retinal disease. To advance our understanding of the etiology blinding diseases, we used single-cell RNA-sequencing (scRNA-seq) to analyze transcriptomes ~85,000 cells fovea and peripheral retina seven adult human donors. Utilizing computational methods, identified 58 cell types within 6 classes: photoreceptor, horizontal, bipolar, amacrine, ganglion non-neuronal cells. Nearly all are shared between two regions, but there notable differences in gene expression proportions foveal cohorts types. We then atlas map 636 genes implicated as causes or risk factors for diseases. Many expressed striking class-, type-, region-specific patterns. Finally, compared signatures cynomolgus macaque monkey, Macaca fascicularis . show that over 90% correspond transcriptomically those previously macaque, disease-related is largely conserved species. These validate use modeling disease, provide a foundation investigating molecular mechanisms underlying visual processing.

Language: Английский

Citations

228
Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression DOI Creative Commons

Jillian J. Goetz,

Zachary F. Jessen,

Anne Jacobi

et al.

Cell Reports, Journal Year: 2022, Volume and Issue: 40(2), P. 111040 - 111040

Published: July 1, 2022

Classification and characterization of neuronal types are critical for understanding their function dysfunction. Neuronal classification schemes typically rely on measurements electrophysiological, morphological, molecular features, but aligning such datasets has been challenging. Here, we present a unified mouse retinal ganglion cells (RGCs), the sole output neurons. We use visually evoked responses to classify 1,859 RGCs into 42 types. also obtain morphological or transcriptomic data from subsets these align functional publicly available datasets. create an online database that allows users browse download light using machine learning algorithm. This work provides resource studies RGCs, upstream circuits in retina, projections brain, establishes framework future efforts open distribution.

Language: Английский

Citations

170