Portuguese
Albanian
Arabic
Armenian
Azerbaijani
Belarusian
Bengali
Bosnian
Catalan
Czech
Danish
Deutsch
Dutch
English
Estonian
Finnish
Français
Greek
Haitian Creole
Hebrew
Hindi
Hungarian
Icelandic
Indonesian
Irish
Italian
Japanese
Korean
Latvian
Lithuanian
Macedonian
Mongolian
Norwegian
Persian
Polish
Portuguese
Romanian
Russian
Serbian
Slovak
Slovenian
Spanish
Swahili
Swedish
Turkish
Ukrainian
Vietnamese
Български
中文(简体)
中文(繁體)
Neuromuscular Disorders 2017-May

Establishing a reference dataset for the authentication of spinal muscular atrophy cell lines using STR profiling and digital PCR.

Apenas usuários registrados podem traduzir artigos
Entrar Inscrever-se
O link é salvo na área de transferência
Deborah L Stabley
Jennifer Holbrook
Ashlee W Harris
Kathryn J Swoboda
Thomas O Crawford
Katia Sol-Church
Matthew E R Butchbach

Palavras-chave

Resumo

Fibroblasts and lymphoblastoid cell lines (LCLs) derived from individuals with spinal muscular atrophy (SMA) have been and continue to be essential for translational SMA research. Authentication of cell lines helps ensure reproducibility and rigor in biomedical research. This quality control measure identifies mislabeling or cross-contamination of cell lines and prevents misinterpretation of data. Unfortunately, authentication of SMA cell lines used in various studies has not been possible because of a lack of a reference. In this study, we provide said reference so that SMA cell lines can be subsequently authenticated. We use short tandem repeat (STR) profiling and digital PCR (dPCR), which quantifies SMN1 and SMN2 copy numbers, to generate molecular identity codes for fibroblasts and LCLs that are commonly used in SMA research. Using these molecular identity codes, we clarify the familial relationships within a set of fibroblasts commonly used in SMA research. This study presents the first cell line reference set for the SMA research community and demonstrates its usefulness for re-identification and authentication of lines commonly used as in vitro models for future studies.

Junte-se à nossa
página do facebook

O mais completo banco de dados de ervas medicinais apoiado pela ciência

  • Funciona em 55 idiomas
  • Curas herbais apoiadas pela ciência
  • Reconhecimento de ervas por imagem
  • Mapa GPS interativo - marcar ervas no local (em breve)
  • Leia publicações científicas relacionadas à sua pesquisa
  • Pesquise ervas medicinais por seus efeitos
  • Organize seus interesses e mantenha-se atualizado com as notícias de pesquisa, testes clínicos e patentes

Digite um sintoma ou doença e leia sobre ervas que podem ajudar, digite uma erva e veja as doenças e sintomas contra os quais ela é usada.
* Todas as informações são baseadas em pesquisas científicas publicadas

Google Play badgeApp Store badge