Dataset Information

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.

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.

SUBMITTER: Lee BR

PROVIDER: S-EPMC8428855 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.

Lee Brian R BR Budzillo Agata A Hadley Kristen K Miller Jeremy A JA Jarsky Tim T Baker Katherine K Hill DiJon D Kim Lisa L Mann Rusty R Ng Lindsay L Oldre Aaron A Rajanbabu Ram R Trinh Jessica J Vargas Sara S Braun Thomas T Dalley Rachel A RA Gouwens Nathan W NW Kalmbach Brian E BE Kim Tae Kyung TK Smith Kimberly A KA Soler-Llavina Gilberto G Sorensen Staci S Tasic Bosiljka B Ting Jonathan T JT Lein Ed E Zeng Hongkui H Murphy Gabe J GJ Berg Jim J

eLife 20210813

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 ...[more]

PMID: 34387544

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Project description:The classical sacrococcygeal chordoma tumor presents with a typical morphology of lobulated myxoid tumor tissue with cords, strands and nests of tumor cells consisting of small non-vacuolated cells, intermediate cells with a wide range of vacuolization and large heavily vacuolated (physaliferous) cells. Because of its rare incidence, lack of suited model systems and technical limitations analysis was only performed on bulk tumor mass neglecting its heterogeneous composition. We aimed at elucidating the differences between small non-vacuolated and large physaliferous cells on the genomic and transcriptomic level. Secondly, we intended to clarify whether the observed cell types are derived from genetically distinct clones or rather represent different phenotypes. Using the chordoma cell line MUG-Chor1 we monitored morphological changes via time lapse experiments. We isolated pure fractions of each phenotype by means of laser microdissection or micromanipulation allowing phenotype-specific analysis. Pools of 100 cells each were genetically profiled after whole genome amplification by array comparative genomic hybridization. For expression analysis 20 cells each were subjected to whole transcriptom amplification, forwarded to RNA microarray analysis and qRT-PCR. Time lapse analysis unveiled small non-vacuolated cells to develop into large physaliferous cells via intermediate cells containing an increasing amount of vacuoles. Furthermore, we showed small and large physaliferous cells to proliferate at the same rate but intermediate cells to be the most proliferating cell phenotype. Small non-vacuolated and large physaliferous cells showed identical copy number variations. Despite their obvious morphological disparities we detected only modest changes in over all gene expression. However, verification of candidate genes yielded significant up-regulation of ALG11 (700-fold), PPP2CB (18.6-fold), and UCHL3 (18.7-fold) in large physaliferous cells. Of two different cell types (large and small MUG-Chor1 cells) we analysed each in triplicates. In total 6 cell pools were analysed.

Dataset Information

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.

Publications

Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization.

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