Name: Author Spotlight: Automated Lifespan Monitoring &#8211; Discovering Aging Dynamics with the Lifespan Machine
Uploaded: 2024-01-26T13:00:00
Description: Watch this Scientific Journal Video about Automated Lifespan Monitoring â€“ Discovering Aging Dynamics with the Lifespan Machine at JoVE.com

We thank Julian Ceron and Jeremy Vicencio (IDIBELL Barcelona) for producing the rpb-2(cer135) allele. This project was funded by the European Research Council (ERC) under the European Union&#39;s Horizon 2020 research and innovation programme (Grant Agreement No. 852201), the Spanish Ministry of Economy, Industry and Competitiveness (MEIC) to the EMBL partnership, the Centro de Excelencia Severo Ochoa (CEX2020-001049-S, MCIN/AEI /10.13039/501100011033), the CERCA Programme/Generalitat de Catalunya, the MEIC Excelencia award BFU2017-88615-P, and an award from the Glenn Foundation for Medical Research.

1. Software and hardware requirements
<ol>
	<li>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.</li>
	<li>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...

Unveiling Aging Mysteries: Lifespan Machine for High-Throughput Insights

<ol>
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Wild-type growth and reproduction.</a> Developmental Biology. 51 (1), 23-33 (1976).</li><li>Perez, M. F., Francesconi, M., Hidalgo-Carcedo, C., Lehner, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Maternal+age+generates+phenotypic+variation+in+Caenorhabditis+elegans.">Maternal age generates phenotypic variation in Caenorhabditis elegans.</a> Nature. 552 (7683), 106-109 (2017).</li><li>Wilkinson, D. S., Taylor, R. 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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...

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 control group. Unfortunately, reproducibility issues pervade aging research, sometimes due to statistically underpowered experiments4 and often because of the inherent sensitivity of lifespan assays to subtle variations in the environment5. Robust experiments require multiple replicates of large populations, and this process particularly benefits from the experimental scalability offered by automation6.
The rigorous demands of lifespan assays originate from the unpredictability of the aging process itself. Isogenic individuals housed in identical environments display different death times and rates of physiological decline7, suggesting that lifespan involves a high degree of stochasticity7,8. Therefore, large populations are required to measure quantitative changes in the aging process, such as changes in the mean or maximum lifespan, and to overcome biases arising from individual variability. In addition, a capacity for high-throughput lifespan assays is crucial to support studies of survival curve shapes and models of the dynamics of aging9.
The nematode Caenorhabditis elegans is an invaluable model for aging research due to its short lifespan, genetic tractability, and rapid generation time, which underscore its suitability for high-throughput aging and lifespan assays. Traditionally, the lifespan in C. elegans has been measured by following a synchronized, small population of about 50-100 animals over time on solid media and writing down the time of individual deaths. As animals age and lose mobility, manually scoring the death times requires individually prodding the animals and checking for small movements of the head or tail. This is usually a tedious and laborious process, though efforts have been made to accelerate it10,11,12. Importantly, slow experimental pipelines hinder progress in our understanding of aging and the effectiveness of tested interventions.
To meet the demands of aging research for quantitative data, many technologies have been developed for automating data collection, including a remarkable range of approaches from microfluidic chambers to flatbed scanners13,14,15,16,17,18. The LSM differs from other methods in its extensive optimization for the collection of highly precise and accurate lifespan data, which is achieved through the development of careful equipment calibration protocols combined with an extensive software suite that allows users to validate, correct, and refine automated analyses13. Though the software can, in principle, be applied to diverse imaging modalities, in practice, most users use flatbed scanners modified to allow for fine-tuned control over the environmental temperature and humidity - factors of critical importance due to their major effect on lifespan19. The LSM takes images of nematodes every 20 min over intervals ranging from days to months, depending on the environmental conditions and genotype. The data produced are of much higher temporal resolution compared to data from manual assays, and the images collected provide a permanent visual record of the nematode position across the lifespan. Using machine-learning methods, death times are automatically assigned to each individual. These results can be rapidly, manually validated using a client software called &#34;Worm Browser&#34;. As a result of its hardware and software, the LSM can generate survival curves that are statistically indistinguishable from manual death scoring at the hands of experienced researchers, with the added advantage of decreased workload and higher scalability13.
The latest version of the LSM also allows for the study of behavioral aging by collecting morphological and behavioral data throughout the nematode&#39;s life and reporting it along with the lifespan of each individual. In particular, the LSM captures the time of each animal&#39;s vigorous movement cessation (VMC), a landmark often used to quantify the &#34;healthspan&#34; of an individual as distinct from its lifespan. By simultaneously collecting lifespan and behavioral aging data, the LSM supports the study of interventions that may have differential effects on different phenotypic outcomes of aging20. A variety of macroscopically observable phenotypes can be used to study behavioral aging, such as body movement or pharyngeal pumping21, tissue integrity22, and movement speed or stimulus-induced turning17. Comparisons between different aging phenotypes can support analyses of the causal structure of aging processes. For instance, the comparison between VMC and lifespan was recently used to characterize two distinct aging processes in C. elegans23.
While initially developed to measure lifespan in C. elegans, the LSM supports the collection of survival and behavioral data from a range of nematode species, including C. briggsae, C. tropicalis, C. japonica, C. brenneri, and P. pacificus23. The technology facilitates the study of the effect of biological and environmental interventions on lifespan, stress resistance, and pathogen resistance and can be coupled to experimental tools such as targeted assays of RNA interference or auxin-inducible protein degradation systems. To date, it has been used in the scientific literature for a wide range of applications6,24,25,26,27,28,29,30.
Here, we outline a step-by-step protocol for performing a Lifespan Machine experiment using agar plates, from the initial stages of the experimental setup to the output of the resulting survival curves. A distinctive feature of the LSM is that the effort is highly front-loaded, meaning that the majority of the researcher&#39;s time is spent during experimental setup and, to a small degree, during post-image&#160;acquisition. The data collection is completely automated for the whole duration of the experiment and allows the researcher to have a &#34;hands-free&#34; experience. The steps described here are held in common among many different types of survival assays - the same experimental setup is performed for lifespan, thermotolerance, oxidative stress, and pathogenesis assays. In the representative results section, we discuss a subset of data from a recently published manuscript to illustrate the effectiveness of the analysis pipeline and highlight the most important steps during image analysis23.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>1-Naphtaleneacetic&#160; acid (Auxin)</td>
			<td>Sigma</td>
			<td>N0640</td>
			<td>Solubilize Auxin in 1M potassium hydroxide and add into molten agar</td>
		</tr>
		<tr>
			<td>5-fluoro-2-deoxyuridine (FUDR)</td>
			<td>Sigma</td>
			<td>F0503</td>
			<td>27.5 &#956;g/mL of FUDR was used to eliminate progeny from populations on UV-inactivated bacteria</td>
		</tr>
		<tr>
			<td>Glass cleaner</td>
			<td>Kristal-M</td>
			<td>QB-KRISTAL-M125ml</td>
			<td></td>
		</tr>
		<tr>
			<td>Hydrophobic anti-fog glass treatment</td>
			<td>Rain-X Scheibenreiniger&#160;</td>
			<td>C. 059140</td>
			<td></td>
		</tr>
		<tr>
			<td>Rubber matt</td>
			<td>Local crafstman</td>
			<td></td>
			<td>Cut on a high-strength EPDM rubber sheet stock</td>
		</tr>
		<tr>
			<td>Scanner glass</td>
			<td>Local hardware supplier</td>
			<td></td>
			<td>9&#34; x 11.5&#34; inch glass sheet</td>
		</tr>
		<tr>
			<td>Scanner plates</td>
			<td>Life Sciences</td>
			<td>351006</td>
			<td>50 mm x 9 mm, polystyrene petri dish</td>
		</tr>
		<tr>
			<td>USB Reference Thermometer</td>
			<td>USB Brando</td>
			<td>ULIFE055500</td>
			<td>&#160;For calibrating temperature of scanners</td>
		</tr>
	</tbody>
</table>

high-throughput behavioral aging and lifespan assays using the lifespan machine

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.

Author Spotlight: Automated Lifespan Monitoring &#8211; Discovering Aging Dynamics with the Lifespan Machine 

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

In our lab, we study the physiological and molecular determinants of aging and lifespan, and we do so in a quantitative manner. For this, we use the nematode C.elegans as a fast-aging genetically tractable model, and we use it with a combination of tools such as the Lifespan Machine. Reproducibility is an important issue in aging research.To overcome this, large population of animals are often required for measuring robust changes in lifespan, and therefore, this process really benefits from automated high-throughput protocols. Lots of people have used the Lifespan Machine for many different purposes and are really happy about that. For our own applications, the two most significant findings are, first, in 2016, our discovery of temporal scaling as a common outcome of many interventions in aging.And second, in 2022, our discovery that healthspan and lifespan are determined by distinct underlying physical declines. The Lifespan Machine adapts commonly used agar plate protocols to collect survival and behavioral aging data from large populations of nematodes. By automation and high-frequency of image acquisition, it eliminates the daily tedious process of by-hand survival assays and gathers previously unattainable datasets on nematode behavior.

Experimental reproducibility in lifespan assays is challenging and requires both tightly controlled experimental conditions and large populations to achieve sufficient statistical resolution4,36. 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 ...

Watch this Scientific Journal Video about Automated Lifespan Monitoring â€“ Discovering Aging Dynamics with the Lifespan Machine at JoVE.com

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.

Genetically identical animals kept in a constant environment display a wide distribution of lifespans, reflecting a large non-genetic, stochastic aspect ...