Functional In vivo Single-cell Transcriptome (FIST) Analysis Reveals Molecular Properties of Light-Sensitive Neurons in Mouse V1

Author:

Liu Jianwei,Pan Na,Sun Le,Wang Mengdi,Zhang Junjing,Zuo Zhentao,He Sheng,Wu Qian,Wang Xiaoqun

Abstract

ABSTRACTVision formation is classically based on projections from the retinal ganglion cells (RGC) to the lateral geniculate nucleus (LGN) and the primary visual cortex (V1). Although the cellular information of the retina and the LGN has been widely studied, the transcriptome profiles of single neurons with specific functions in V1 still remain unknown. Some neurons in mouse V1 are tuned to light stimulus. To determine the molecular properties of light-stimulated neurons in layer 2/3 of V1, we developed a method of functional in vivo single-cell transcriptome (FIST) analysis that integrates sensory evoked calcium imaging, whole-cell electrophysiological patch-clamp recordings, single-cell mRNA sequencing and three-dimensional morphological characterization in a live mouse, based on a two-photon microscope system. In our study, 58 individual cells from layer 2/3 of V1 were identified as either light-sensitive (LS) or non-light-sensitive (NS) by single-cell light-evoked calcium evaluation and action potential spiking. The contents of every single cell after individual functional tests were aspirated through the patch-clamp pipette for mRNA sequencing. Furthermore, the three-dimensional (3-D) morphological characterizations of the neurons were reconstructed in the live mouse after the whole-cell recordings. Our sequencing results indicated that V1 neurons with high expression of genes related to transmission regulation, such as Rtn4r, Nr4a1, and genes involved in membrane transport, such as Na+/K+ ATPase, NMDA-type glutamatergic receptor, preferentially respond to light stimulation. Our findings demonstrate the ability of FIST analysis to characterize the functional, morphological and transcriptomic properties of a single cell in alive animal, thereby providing precise neuronal information and predicting its network contribution in the brain.

Publisher

Cold Spring Harbor Laboratory

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