Descripción del proyecto
Una plataforma informática para caracterizar la regulación génica a nivel de célula única
El desarrollo de tecnologías de célula única ha permitido caracterizar con mayor precisión los tipos celulares y los patrones subyacentes a los procesos del desarrollo, mientras que la integración de datos de diferentes análisis ómico ha permitido obtener una imagen más nítida de las relaciones mecanicistas. El objetivo del proyecto GReCS, financiado por las Acciones Marie Skłodowska-Curie, es desarrollar un método informático que proporcione información sobre la regulación génica a nivel de célula única. El método integrará datos transcriptómicos y de la cromatina abierta para filtrar información previa sobre interacciones candidatas y predecir redes de regulación génica de células específicas a través del aprendizaje automático. El juego de herramientas informáticas desarrollado estará a disposición de la comunidad, lo que permitirá ampliar la caracterización de la regulación génica mediante la combinación de diferentes tipos de datos.
Objetivo
The advent of single cell technologies has enabled the characterization of cell types and developmental processes. Observations from different cells allow one to identify underlying patterns at higher resolution than convoluted bulk data, and integration of different omics data can yield a more differentiated picture of mechanistic connections. In this proposal, Gene REgulatory Cell States (GReCS) from multi-modal data, I plan to develop a computational method that combines these aspects to generate insights into gene regulation at the level of single cells.
Measurements of chromatin accessibility in single cells are becoming increasingly common. The method I propose to develop combines sc/sn-ATAC- and scRNA-sequencing data to characterize gene regulation. My approach will integrate and use transcriptomics and open chromatin data to filter comprehensive prior information about candidate interactions and predict cell-specific gene regulatory network versions using machine learning, while sparse single cell measurements are imputed using local cell similarities. In this way, rare measurements across cell types and a larger condition space for network inference can be exploited, using the natural potential of chromatin accessibility data as a filter to map interactions into a cell-specific context.
A distinguishing feature of the proposed method is the characterization of local gene regulatory states, which allows the observation of continuous changes throughout a cell-cell similarity embedding. This will be useful to examine changes during cell differentiation and along gradients in spatial reconstructions, for example of embryonic development. The developed methods will be made available to the community as a computational toolkit to improve the characterization of gene regulation by combining different types of data.
Ámbito científico
Palabras clave
Programa(s)
Régimen de financiación
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinador
CB10 1SA SAFFRON WALDEN
Reino Unido