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

Summary

The imaging platform "The Lifespan Machine" automates the lifelong observation of large populations. We show the steps required to perform lifespan, stress resistance, pathogenesis, and behavioral aging assays. The quality and scope of the data allow researchers to study interventions in aging despite the presence of biological and environmental variation.

Abstract

Genetically identical animals kept in a constant environment display a wide distribution of lifespans, reflecting a large non-genetic, stochastic aspect to aging conserved across all organisms studied. This stochastic component means that in order to understand aging and identify successful interventions that extend the lifespan or improve health, researchers must monitor large populations of experimental animals simultaneously. Traditional manual death scoring limits the throughput and scale required for large-scale hypothesis testing, leading to the development of automated methods for high-throughput lifespan assays. The Lifespan Machine (LSM) is a high-throughput imaging platform that combines modified flatbed scanners with custom image processing and data validation software for the life-long tracking of nematodes. The platform constitutes a major technical advance by generating highly temporally resolved lifespan data from large populations of animals at an unprecedented scale and at a statistical precision and accuracy equal to manual assays performed by experienced researchers. Recently, the LSM has been further developed to quantify the behavioral and morphological changes observed during aging and relate them to lifespan. Here, we describe how to plan, run, and analyze an automated lifespan experiment using the LSM. We further highlight the critical steps required for the successful collection of behavioral data and high-quality survival curves.

Introduction

Aging is a complex, multifaceted process characterized by a decline in the physiological function of an organism, which leads to an increase in the risk of disease and death over time¹. Lifespan, measured as the time from birth or the onset of adulthood until death, provides an unambiguous outcome of aging² and an indirect but rigorously quantitative proxy for measuring the relative rate of aging between populations³. Aging studies often depend on accurate measurements of lifespan, similar to clinical trials, to compare outcomes between one population exposed to an intervention and an unexposed co....

Protocol

1. Software and hardware requirements

1. Flatbed scanners: In principle, the LSM can be implemented using a variety of imaging devices. Detailed instructions for scanner modifications and focusing are available elsewhere¹³. The LSM hardware is shown in Supplementary Figure 1.
2. Data analysis tools: The LSM software has three interacting components: a Linux-based scanner control software package, a web-browser-based metadata management package, and a Windows and Linux client image analysis software package. Refer to the instructions for installing software tools published on the GitHub repositor....

Results

Experimental reproducibility in lifespan assays is challenging and requires both tightly controlled experimental conditions and large populations to achieve sufficient statistical resolution^4,36. The LSM is uniquely suitable for surveying large populations of animals in a constant environment with high temporal resolution. To demonstrate the capability of the LSM, highlight the crucial steps of analysis, and help researchers to prioritize their labor efforts, we .......

Discussion

Here, we provide a detailed, accessible protocol for performing an experiment using the latest version of the Lifespan Machine. We have shown that the critical step for achieving well-resolved survival curves is the manual exclusion of non-worm objects during post-image acquisition. Manual death time annotation has a small effect on the overall shape of the survival curves, demonstrating that fully automated death time estimation is efficient even without manual annotation (Figure 8). On the.......

Materials

Name	Company	Catalog Number	Comments
1-Naphtaleneacetic  acid (Auxin)	Sigma	N0640	Solubilize Auxin in 1M potassium hydroxide and add into molten agar
5-fluoro-2-deoxyuridine (FUDR)	Sigma	F0503	27.5 μg/mL of FUDR was used to eliminate progeny from populations on UV-inactivated bacteria
Glass cleaner	Kristal-M	QB-KRISTAL-M125ml
Hydrophobic anti-fog glass treatment	Rain-X Scheibenreiniger	C. 059140
Rubber matt	Local crafstman		Cut on a high-strength EPDM rubber sheet stock
Scanner glass	Local hardware supplier		9" x 11.5" inch glass sheet
Scanner plates	Life Sciences	351006	50 mm x 9 mm, polystyrene petri dish
USB Reference Thermometer	USB Brando	ULIFE055500	For calibrating temperature of scanners