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High-throughput scNMT protocol for multiomics profiling of single cells from mouse brain and pancreatic organoids.
Cerrizuela, Santiago; Kaya, Oguzhan; Kremer, Lukas P M; Sarvari, Andrea; Ellinger, Tobias; Straub, Jannes; Brunken, Jan; Sanz-Morejón, Andrés; Korkmaz, Aylin; Martín-Villalba, Ana.
Afiliación
  • Cerrizuela S; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany. Electronic address: s.cerrizuela@dkfz-heidelberg.de.
  • Kaya O; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Kremer LPM; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Sarvari A; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Ellinger T; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Straub J; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Brunken J; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Sanz-Morejón A; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Korkmaz A; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany.
  • Martín-Villalba A; Molecular Neurobiology, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany. Electronic address: a.martin-villalba@dkfz-heidelberg.de.
STAR Protoc ; 3(3): 101555, 2022 09 16.
Article en En | MEDLINE | ID: mdl-36072757
Single-cell nucleosome, methylome, and transcriptome (scNMT) sequencing is a recently developed method that allows multiomics profiling of single cells. In this scNMT protocol, we describe profiling of cells from mouse brain and pancreatic organoids, using liquid handling platforms to increase throughput from 96-well to 384-well plate format. Our approach miniaturizes reaction volumes and incorporates the latest Smart-seq3 protocol to obtain higher numbers of detected genes and genomic DNA (gDNA) CpGs per cell. We outline normalization steps to optimally distribute per-cell sequencing depth. For complete details on the use and execution of this protocol, please refer to Clark (2019), Clark et al. (2018), and Clark et al., 2018, Hagemann-Jensen et al., 2020a, Hagemann-Jensen et al., 2020b.
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Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Nucleosomas / Epigenoma Límite: Animals Idioma: En Revista: STAR Protoc Año: 2022 Tipo del documento: Article

Texto completo: 1 Banco de datos: MEDLINE Asunto principal: Nucleosomas / Epigenoma Límite: Animals Idioma: En Revista: STAR Protoc Año: 2022 Tipo del documento: Article