High-Throughput Behavioral Aging and Lifespan Assays Using the Lifespan Machine.

Area of Science:

• Gerontology
• Biotechnology
• Animal models

Background:

• Aging exhibits significant stochastic variability, necessitating large-scale studies for understanding lifespan and health interventions.
• Manual lifespan scoring methods limit experimental throughput and scalability for aging research.
• Automated high-throughput lifespan assays are crucial for advancing the study of aging.

Purpose of the Study:

• To describe the planning, execution, and analysis of automated lifespan experiments using the Lifespan Machine (LSM).
• To highlight critical steps for collecting behavioral data and generating high-quality survival curves.
• To showcase the LSM's capability for high-throughput, precise lifespan tracking and aging analysis.

Main Methods:

• Utilized the Lifespan Machine (LSM), a high-throughput imaging platform with modified scanners and custom software.
• Implemented lifelong tracking of nematodes for automated, high-resolution lifespan data collection.
• Developed image processing and data validation for accurate survival curve generation and behavioral analysis.

Main Results:

• The LSM platform achieves unprecedented scale and statistical precision in lifespan assays, matching manual methods.
• The developed system enables quantification of age-related behavioral and morphological changes.
• High-quality survival curves and behavioral data can be successfully collected using the LSM.

Conclusions:

• The Lifespan Machine represents a significant technical advancement for large-scale aging research.
• Automated tracking and behavioral analysis using LSM enhance the understanding of aging processes.
• This platform facilitates hypothesis testing and the identification of lifespan-extending interventions.