Abstract
The Patch-seq approach is a powerful variation of the patch-clamp technique that allows for the combined electrophysiological, morphological, and transcriptomic characterization of individual neurons. To generate Patch-seq datasets at scale, we identified and refined key factors that contribute to the efficient collection of high-quality data. We developed patch-clamp electrophysiology software with analysis functions specifically designed to automate acquisition with online quality control. We recognized the importance of extracting the nucleus for transcriptomic success and maximizing membrane integrity during nucleus extraction for morphology success. The protocol is generalizable to different species and brain regions, as demonstrated by capturing multimodal data from human and macaque brain slices. The protocol, analysis and acquisition software are compiled at https://githubcom/AllenInstitute/patchseqtools. This resource can be used by individual labs to generate data across diverse mammalian species and that is compatible with large publicly available Patch-seq datasets.
Highlights
Describing and understanding the properties of neuronal cell types is a critical first step towards understanding circuit activity within the brain, and cognitive function
The expanded dataset used for the analysis presented here includes cells that did not pass the stringent quality control (QC) metrics required to be part of that previous study
To compare neurons within similar neuron types, we focused on four established Cre lines: retinol binding protein 4 (Rbp4)-Cre for glutamatergic neurons and parvalbumin (Pvalb)-Cre, somatostatin (Sst)-Cre, and vasoactive intestinal peptide (Vip)-Cre for GABAergic neurons
Summary
Describing and understanding the properties of neuronal cell types is a critical first step towards understanding circuit activity within the brain, and cognitive function. Systematic and large-scale scRNA-seq approaches have been successful at characterizing brain cell types across mammalian species (Yao et al 2021; Tasic et al 2016; Tasic et al 2018; Bakken et al 2018; Hodge et al 2019; Hashikawa et al 2020; Zeisel et al 2015; Mickelsen et al 2019). These large-scale studies often include data from tens of thousands to millions of neurons whereas electrophysiological or morphological studies are limited to tens or hundreds of neurons. Studies with triple modality data are rare and lack the scale to capture the true biological variability
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