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Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression.

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

SUBMITTER: Goetz J

PROVIDER: S-EPMC9364428 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression.

Goetz Jillian J Jessen Zachary F ZF Jacobi Anne A Mani Adam A Cooler Sam S Greer Devon D Kadri Sabah S Segal Jeremy J Shekhar Karthik K Sanes Joshua R JR Schwartz Gregory W GW

Cell reports 20220701 2

Classification and characterization of neuronal types are critical for understanding their function and dysfunction. Neuronal classification schemes typically rely on measurements of electrophysiological, morphological, and molecular features, but aligning such datasets has been challenging. Here, we present a unified classification of mouse retinal ganglion cells (RGCs), the sole retinal output neurons. We use visually evoked responses to classify 1,859 mouse RGCs into 42 types. We also obtain ...[more]

PMID: 35830791

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Project description:The retinal ganglion cells (RGCs) have diverse morphology and physiology. Although some studies show that correlations between morphological properties and physiological properties exist in cat RGCs, these properties are much less distinct and their correlations are unknown in mouse RGCs. In this study, using three-dimensional digital neuron reconstruction, we systematically analyzed twelve morphological parameters of mouse RGCs as they developed in the first four postnatal weeks. The development of these parameters fell into three different patterns and suggested that contact from bipolar cells and eye opening might play important roles in RGC morphological development. Although there has been a general impression that the morphological parameters are not independent, such as RGCs with larger dendritic fields usually have longer but sparser dendrites, there was not systematic study and statistical analysis proving it. We used Pearson's correlation coefficients to determine the relationship among these morphological parameters and demonstrated that many morphological parameters showed high statistical correlation. In the same cells we also measured seven physiological parameters using whole-cell patch-clamp recording, focusing on intrinsic excitability. We previously reported the increase in intrinsic excitability in mouse RGCs during early postnatal development. Here we showed that strong correlations also existed among many physiological parameters that measure the intrinsic excitability. However, Pearson's correlation coefficient revealed very limited correlation across morphological and physiological parameters. In addition, principle component analysis failed to separate RGCs into clusters using combined morphological and physiological parameters. Therefore, despite strong correlations within the morphological parameters and within the physiological parameters, postnatal mouse RGCs had only limited correlation between morphology and physiology. This may be due to developmental immaturity, or to selection of parameters.

Dataset Information

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression.

Publications

Unified classification of mouse retinal ganglion cells using function, morphology, and gene expression.

Similar Datasets

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