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Updated: Mar 28, 2026

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells

Published on: May 22, 2017

Electrophysiological, transcriptomic and morphologic profiling of single neurons using Patch-seq.

Cathryn R Cadwell¹, Athanasia Palasantza^2,3, Xiaolong Jiang¹

¹Department of Neuroscience, Baylor College of Medicine, Houston, Texas, USA.

Nature Biotechnology

|December 23, 2015

Summary

This summary is machine-generated.

We developed Patch-seq, a novel method combining electrophysiology, RNA sequencing, and morphology to profile individual neocortical neurons. This technique links gene expression to neuronal function and structure, advancing cell type classification in the nervous system.

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Area of Science:

Neuroscience
Genomics
Cell Biology

Background:

The mammalian neocortex is crucial for cognition, but its cellular components remain incompletely understood.
Accurate classification of neuronal cell types is essential for understanding brain function.

Purpose of the Study:

To introduce and validate Patch-seq, a method for comprehensive single-neuron characterization.
To correlate gene expression with physiological and morphological properties of neocortical neurons.

Main Methods:

Patch-seq integrates whole-cell electrophysiological recordings, single-cell RNA sequencing, and morphological analysis.
Cellular contents are aspirated post-recording for subsequent molecular profiling.
Data from 58 neocortical cells were analyzed.

Main Results:

Gene expression patterns accurately predict morphological and physiological neuronal properties.
Specific gene expression profiles correlate with axonal arborization and action potential amplitude.
Established electrophysiological and molecular profiles for 58 individual neocortical neurons.

Conclusions:

Patch-seq provides a powerful approach to decipher neuronal diversity.
This method facilitates the classification of neuronal cell types based on integrated molecular and functional data.
Results illuminate the molecular basis of neuronal heterogeneity in the neocortex.