BOWiD: a tool for lifelong tracking C. elegans individuals using stochastically labelled landmarks
openNIA - National Institute on Aging
Project Summary
Caenorhabditis elegans, a well-studied nematode, is an important model to study the genetic mechanisms that
regulate aging, longevity, and health. Most aging studies in this model organism measure the survival of a
population through time, and factors that extend or shorten lifespan are observed by a shift of these survival
curves. An important limitation of this type of analysis, is that individual animals cannot be tracked, and thus the
identity of each individual is lost in each day where survival is scored. Tracing the aging trajectories of individual
animals within a population is necessary to better understand how early life events affect the rate of aging and
longevity. In addition, populations tend to show very variable lifespans even though they are genetically identical,
and are exposed to the exact same conditions. Better understanding this variability and how different
interventions can modulate longevity differently in different animals can only be possible if animal identity can be
detected. Here, we propose to develop a new system, BOWiD, which provides a readable and stable barcode
for each animal in a population, and thus allows extracting the identity of each worm. Barcodes will be imprinted
in animals by turning on a fluorescent protein in a subset of cells. Reporters will be activated randomly by the
Cre-lox recombination system. To accomplish this, we will focus on: 1) Developing robust components needed
for BOWiD, including inducible promoters for Cre, identify robust cellular landmarks and labeling approaches,
and developing machine learning pipelines to read barcodes, 2) Developing and implementing BOWiD for
animals cultured on traditional agar plates, and analyze aging trajectories, and 3) Developing and implementing
BOWiD for animals cultured in microfluidic devices, where trajectories of the structure of aging neurons will be
analyzed, in a high-resolution setup.
Up to $581K
health research