Lifetime fet flagship

22 September 2018
FET-Flag LifeTime project is an unprecedented European scientific endeavor launched by a consortium of 60 scientists from all across the continent. Their goal is to track, understand and predict how the molecular make-up of cells changes in human diseases, and ultimately, how to intervene. Stein Aerts, Yvan Saeys and Jean-Christophe Marine are three Belgian partners in this very ambitious project.

New techniques
The success of the project relies heavily on technology development. The scientists need new ways to extract DNA, RNA, and proteins from individual cells, at high-throughput scale. Recording the spatial location and all biological parameters of each individual cell within a tissue will generate a gigantic amount of multidimensional data.

Stein Aerts: “We are keen on inventing new bioinformatics and machine learning algorithms to analyze and model which genes are active in individual cells. A variety of genome-wide information layers or “omics” data will be generated for millions, perhaps even billions of single cells. We’ll need smart ways of making sense of this data if we want to use it to make valuable predictions for patients, including disease outcome, therapy choice, or prognosis.”

New insights
This is where cancer experts such as Jean-Christophe Marine come in. His team will exploit the single-cell methods to profile large amounts of single cells from healthy tissues and tumors. Applications extend to many other diseases besides cancer and both researchers underscore the importance of teamwork in this large endeavor. Jean-Christophe Marine: “Single-cell biology is a new field that combines multiple disciplines. This is why being part of this consortium, together with experts in technology development, bioinformatics and systems biology, is so critical and exciting for us.”

Prof. Prof. Yvan Saeys (VIB-UGent Center for Inflammation Research): "Lifetime will bring together researchers from many disciplines, including leading teams in the area of machine learning, statistics and artificial intelligence.  This will allow us to develop and validate novel machine learning methods for visualization, integration, and modeling of cellular processes into unprecedented detail.  This large consortium will also facilitate better benchmarking of tools and techniques, and developing new standards for efficient exchange of data and models, a pressing need in the whole single cell field."​

Follow on Twitter: @LifeTimeFET

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   'Single Cell at ​​VIB'