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Substrate Generation for Endonucleases of CRISPR/Cas Systems
Published on: September 8, 2012
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Registro transcripcional por adquisición de espaciador CRISPR desde el ARN
Florian Schmidt1, Mariia Y Cherepkova1, Randall J Platt2,3
1Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
Nature
|October 5, 2018
Resumen
Este estudio introduce un nuevo método basado en CRISPR para registrar la actividad del ARN celular como ADN. Esta tecnología permite el seguimiento de comportamientos celulares complejos y respuestas a diversos estímulos a lo largo del tiempo.
Área de la Ciencia:
- Biología molecular
- La genética
- Biotecnología
Sus antecedentes:
- Comprender el comportamiento celular requiere rastrear eventos moleculares a lo largo del tiempo.
- Las tecnologías de registro molecular existentes tienen limitaciones en la captura de diversos estímulos.
Objetivo del estudio:
- Desarrollar un nuevo método para registrar la actividad del ARN intracelular a lo largo del tiempo.
- Para permitir el almacenamiento basado en ADN de la información de transcripción para el análisis del estado celular.
Principales métodos:
- Utilizó la adquisición de espaciador CRISPR para convertir el ARN intracelular en ADN.
- Se aplicó el método en Escherichia coli para registrar respuestas a estímulos definidos y complejos.
- Desarrolló un enfoque de secuenciación profunda (Record-seq) para la reconstrucción de la historia de la transcripción.
Principales resultados:
- Se han demostrado registros transcripcionales cuantificables en respuesta a virus de ARN, secuencias arbitrarias y estrés oxidativo.
- Mostró la capacidad de clasificar comportamientos celulares complejos utilizando registros transcripcionales.
- Identificó genes específicos involucrados en respuestas celulares diferenciales.
Conclusiones:
- La adquisición de espaciadores CRISPR proporciona una herramienta poderosa para el registro transcripcional de alta resolución.
- Record-seq permite la reconstrucción de las historias de transcripción celular.
- Esta tecnología tiene aplicaciones potenciales en la comprensión de estados y enfermedades biológicas complejas.
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