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Nollen, E. A.

Publications and source records attributed to Nollen, E. A..

2 recordsLinked to original sources

Deep behavioural phenotyping reveals divergent trajectories of ageing and quantifies health state in C. elegans

Neurodegenerative diseases may be the cause or the consequence of an acceleration of physiological ageing. Evidence for this concept is lacking due to practical limitations of human studies. Here, we compared the processes of physiological and pathological ageing of individual C. elegans over their lifespan. Using multi-parametric phenotyping, trajectories of ageing can be defined within a phenotypic landscape made of a large set of phenotypical features. Rather than an acceleration of ageing, a model for synucleinopathy showed a divergent trajectory of ageing. The pathological progression in individual animals can be predicted from early phenotypes with high accuracy. Despite of similar lifespans, disease-model worms display an early onset of decline in their phenotypic range of ability. This loss of flexibility provides an index of health valid for physiological and pathological contexts. Finally, we demonstrate the power of multi-parametric dataset to describe ageing, to quantify health and to predict specific health risks.

systems biology↗

An open source platform for analyzing and sharing worm behavior data

Animal behavior is increasingly being recorded in systematic imaging studies that generate large data sets. To maximize the usefulness of these data there is a need for improved resources for analyzing and sharing behavior data that will encourage re-analysis and method development by computational scientists1. However, unlike genomic or protein structural data, there are no widely used standards for behavior data. It is therefore desirable to make the data available in a relatively raw form so that different investigators can use their own representations and derive their own features. For computational ethology to approach the level of maturity of other areas of bioinformatics, we need to address at least three challenges: storing and accessing video files, defining flexible data formats to facilitate data sharing, and making software to read, write, browse, and analyze the data. We have developed an open resource to begin addressing these challenges using worm tracking as a model.

animal behavior and cognition↗