Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization
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 t...
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eLife Sciences Publications Ltd
2021
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oai:doaj.org-article:34e4a68dbc504e52805f073d4e7d30602021-11-16T14:02:49ZScaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization10.7554/eLife.654822050-084Xe65482https://doaj.org/article/34e4a68dbc504e52805f073d4e7d30602021-08-01T00:00:00Zhttps://elifesciences.org/articles/65482https://doaj.org/toc/2050-084XThe 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.Brian R LeeAgata BudzilloKristen HadleyJeremy A MillerTim JarskyKatherine BakerDiJon HillLisa KimRusty MannLindsay NgAaron OldreRam RajanbabuJessica TrinhSara VargasThomas BraunRachel A DalleyNathan W GouwensBrian E KalmbachTae Kyung KimKimberly A SmithGilberto Soler-LlavinaStaci SorensenBosiljka TasicJonathan T TingEd LeinHongkui ZengGabe J MurphyJim BergeLife Sciences Publications Ltdarticlepatch-seqelectrophysiologymorphologytranscriptomicsRNA-seqMedicineRScienceQBiology (General)QH301-705.5ENeLife, Vol 10 (2021) |
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DOAJ |
| language |
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| topic |
patch-seq electrophysiology morphology transcriptomics RNA-seq Medicine R Science Q Biology (General) QH301-705.5 |
| spellingShingle |
patch-seq electrophysiology morphology transcriptomics RNA-seq Medicine R Science Q Biology (General) QH301-705.5 Brian R Lee Agata Budzillo Kristen Hadley Jeremy A Miller Tim Jarsky Katherine Baker DiJon Hill Lisa Kim Rusty Mann Lindsay Ng Aaron Oldre Ram Rajanbabu Jessica Trinh Sara Vargas Thomas Braun Rachel A Dalley Nathan W Gouwens Brian E Kalmbach Tae Kyung Kim Kimberly A Smith Gilberto Soler-Llavina Staci Sorensen Bosiljka Tasic Jonathan T Ting Ed Lein Hongkui Zeng Gabe J Murphy Jim Berg Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| description |
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. |
| format |
article |
| author |
Brian R Lee Agata Budzillo Kristen Hadley Jeremy A Miller Tim Jarsky Katherine Baker DiJon Hill Lisa Kim Rusty Mann Lindsay Ng Aaron Oldre Ram Rajanbabu Jessica Trinh Sara Vargas Thomas Braun Rachel A Dalley Nathan W Gouwens Brian E Kalmbach Tae Kyung Kim Kimberly A Smith Gilberto Soler-Llavina Staci Sorensen Bosiljka Tasic Jonathan T Ting Ed Lein Hongkui Zeng Gabe J Murphy Jim Berg |
| author_facet |
Brian R Lee Agata Budzillo Kristen Hadley Jeremy A Miller Tim Jarsky Katherine Baker DiJon Hill Lisa Kim Rusty Mann Lindsay Ng Aaron Oldre Ram Rajanbabu Jessica Trinh Sara Vargas Thomas Braun Rachel A Dalley Nathan W Gouwens Brian E Kalmbach Tae Kyung Kim Kimberly A Smith Gilberto Soler-Llavina Staci Sorensen Bosiljka Tasic Jonathan T Ting Ed Lein Hongkui Zeng Gabe J Murphy Jim Berg |
| author_sort |
Brian R Lee |
| title |
Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| title_short |
Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| title_full |
Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| title_fullStr |
Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| title_full_unstemmed |
Scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| title_sort |
scaled, high fidelity electrophysiological, morphological, and transcriptomic cell characterization |
| publisher |
eLife Sciences Publications Ltd |
| publishDate |
2021 |
| url |
https://doaj.org/article/34e4a68dbc504e52805f073d4e7d3060 |
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1718426411689050112 |