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Un mapa de conectividad de próxima generación: la plataforma L1000 y los primeros 1.000.000 de perfiles
Aravind Subramanian1, Rajiv Narayan1, Steven M Corsello2
1Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA.
Cell
|December 2, 2017
Resumen
El mapa de conectividad (CMap) se ha multiplicado por 1.000 utilizando el método de perfilado L1000. Este CMap expandido conecta genes, medicamentos y enfermedades a través de firmas de expresión génica para el descubrimiento de fármacos y aplicaciones clínicas.
Área de la Ciencia:
- La genómica
- Biología de los sistemas
- Farmacogenómica
Sus antecedentes:
- El Mapa de Conectividad (CMap) vincula los estados biológicos a través de firmas de expresión génica.
- Las versiones anteriores de CMap eran limitadas en escala y alcance.
Objetivo del estudio:
- Informar de una escala 1.000 veces mayor del CMap utilizando la tecnología L1000.
- Demostrar la utilidad del CMap ampliado para el descubrimiento biológico y las aplicaciones clínicas.
Principales métodos:
- Desarrolló e implementó un método de perfilado de expresión de representación reducida de bajo costo y alto rendimiento denominado L1000.
- Generó más de 1,3 millones de perfiles L1000 como parte del consorcio NIH LINCS.
- Se ha validado la reproducibilidad de L1000 y su capacidad para inferir computacionalmente la expresión de transcripción no medida.
Principales resultados:
- El CMap ampliado aumenta significativamente la escala de datos de expresión génica disponibles.
- El perfilado L1000 es altamente reproducible y comparable a la secuenciación de ARN.
- La inferencia computacional predijo con éxito los niveles de expresión para el 81% de las transcripciones no medidas.
- El CMap ampliado facilita el descubrimiento de mecanismos de acción de moléculas pequeñas.
- Ayuda en la anotación funcional de las variantes genéticas en los genes de la enfermedad.
- Informa el diseño del ensayo clínico y la estratificación del paciente.
Conclusiones:
- El CMap ampliado, impulsado por L1000, representa un gran avance en la farmacología de sistemas.
- Este recurso permite nuevos conocimientos sobre los mecanismos de los medicamentos, la genética de las enfermedades y la medicina personalizada.
- Los datos y las herramientas de análisis están disponibles públicamente en https://clue.io.
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