Name: high-throughput behavioral aging and lifespan assays using the lifespan machine
Uploaded: 2024-01-26T13:00:00.000+00:00
Duration: 8 min 53 s
Description: Watch this Scientific Journal Video about Automated Lifespan Monitoring – 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.

Unveiling Aging Mysteries: Lifespan Machine for High-Throughput Insights

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The authors declare that they have no competing interests.

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 contrary, the acquisition of high-quality behavioral aging data requires more careful annotation of the death times, especially in long-lived individuals (Figure 9). Therefore, the amount of time required for by-hand storyboard annotation will ultimately depend on the specific outcome being measured. Overall, we find that when working with the LSM, the researcher&#39;s efforts are most crucial during the experimental setup and, to a smaller degree, during the post-image acquisition analysis. Lastly, we highlight the value of high-throughput, automated assays in producing highly resolved survival and behavioral aging data while increasing the productivity of researchers and supporting experimental reproducibility.
The LSM houses nematodes on agar plates, recreating the classical by-hand lifespan assay in an automated context. Other tools have been developed to automate the measurement of lifespan in C. elegans using different methods of nematode confinement. These include approaches in which single nematodes are housed in solid media (WorMotel15) or within a microfluidic device (Lifespan-on-a-chip11) or those that track a larger population of animals using microfluidics (WormFarm14). The advantages of microfluidic platforms include the possibility of precise, real-time environmental control and the automated removal of progeny by size exclusion. However, the aforementioned devices so far have not proved easily scalable, as they require extensive manual handling and often rely on daily image or video capture triggered by an experimentalist. Other platforms, such as the Stress-Chip39, use the modified flatbed scanners designed for the LSM to image custom microfluidic devices.
In contrast to other methods, the LSM has extensive data annotation and validation facilities, thus allowing users to systematically perform the quality control required to collect high-resolution, precise, and accurate lifespan data in a high-throughput context13. The technique is versatile due to its use of current agar-plate-based laboratory protocols and offers a unique advantage for experimental scalability due to the relative ease of arraying large groups of flatbed scanners. Though the LSM requires an initial time investment to build and operate and to train users on the specialized software, these costs are balanced by the robust, high-throughput production of lifespan data. Lifespan Machine clusters of 50 scanners or more have been deployed in several labs, running continuously for more than a decade40.
The LSM does have limitations. Animals are housed in scanners during the automated collection of survival data, thus limiting the ability of researchers to perform experimental interventions simultaneous to observation and requiring either sterilization of the animals or starting experiments after the nematodes&#39; reproductive phase. Changes in temperature are a rare exception to the limitation on interventions, as the environmental temperature can be modulated without the need to open the scanners and access the animals. In cases where hands-on interventions must be applied to the nematodes mid-life, a common solution is to delay the start of automated observation until after the necessary handling of the animals has been performed. Additionally, there is an inherent variation in the location of plates within and across the scanners. These can be subject to minute, local differences in environmental conditions (such as in temperature, light, ventilation, etc.), which might influence&#160;C. elegans lifespan19. This environmental influence can be further quantified and studied by using accelerated failure time regression models41. A way to mitigate this effect is by simply scaling the number of plates and scanners to achieve a rigorous measurement independent of environmental fluctuations. Commonly, plates of the same condition are randomly distributed within each scanner, and population sizes larger than 500 individuals per condition and across scanners have demonstrated statistically robust survival estimates31.
Altogether, the LSM allows for the high-accuracy and large-population collection of survival and behavioral aging data, and could enable previously unfeasible screens to be performed in a quantitative manner. In this way, the LSM contributes a major technical advance for the standardized collection of survival curves and provides a novel framework for the coupled study of lifespan and behavioral aging in nematodes.

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><tbody><tr><td>1-Naphtaleneacetic&amp;nbsp; 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 &amp;mu;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&amp;nbsp;</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&quot; x 11.5&quot; 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>&amp;nbsp;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.

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 present a subset of data from an optimal, previously published experiment23. Due to the nature of this experiment as a dose-response assay, a substantial number of animals were required for reliably detecting lifespan alterations. By hand, this experiment would require an intense time commitment from multiple researchers or, if scaled down, lead to underpowered results. The dataset measured quantitative changes in lifespan after removing the RNA polymerase II (b) subunit gene (RPB-2), required for messenger RNA transcription. Using the auxin-inducible system37 in C. elegans, the endogenous locus of RPB-2 was tagged with a degron sequence to conditionally ubiquitylate and degrade RPB-2 using different concentrations of auxin (&#945;-naphtaleneacetic acid). The experiment was performed on AMP100 [rpb-2 (cer135); eft-3p::TIR-1] with cer135 corresponding to the CRISPR-inserted AID tag rpb-2::GFP&#916;piRNA::AID::3xFLAG23.&#160;The experimental conditions were at a temperature of 20 &#176;C and using UV-inactivated NEC937 (OP50 &#916;uvrA; KanR)38&#160;E. coli. Hermaphrodites&#160;were sterilized by transferring nematodes during the late L4 stage to plates containing 5-fluoro-2-deoxyuridine (FUdR). Nematodes were transferred during day 2 of adulthood to plates containing different concentrations of auxin. In the original study, the authors showed that in the presence of auxin, RPB-2 degradation shortened&#160;lifespan in a dose-dependent manner23.
Here, we demonstrate the contribution that post-experiment data validation and annotation make to the final survival curve output (Figure 8). In the last steps of image analysis within the Worm Browser storyboard, we compared raw survival curves produced before storyboard annotation to the survival curves produced after the manual exclusion of non-worm objects and also to the survival curves produced after the annotation of both non-worm objects and individual death times (Figure 8A-C). We found that the initial survival curves produced before storyboard annotation (Figure 8A) were distorted by the improper inclusion of non-worm objects, which was most apparent in the tails of the survival curves. Before manual annotation, the survival curves included all objects detected by the machine as potential worm objects, approximately half of which, due to the intentionally high false positive rate, were non-worm objects and had to be manually excluded (Figure 8D). This is by design, as the algorithms are calibrated to have a relatively high false-positive rate in order to avoid false negatives, as it is much easier to exclude incorrectly included objects than to search for and recover incorrectly excluded objects. After excluding non-worm objects during manual storyboard annotation, the resulting survival curves were of much higher resolution (Figure 8B,E), and further manual annotation of the death times on the storyboard produced changes of no more than ~4% in the estimated mean lifespan. We, therefore, demonstrate that the removal of non-worm objects during the image analysis pipeline of the LSM is the crucial step for obtaining well-resolved survival curves.
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/65462/65462fig08.jpg" /> 
Figure 8: The effect of manual data validation on survival curves. The degradation of RPB-2 shortens the C. elegans lifespan in the presence of auxin (K-NAA: &#945;-napthaleneacetic acid) in a dose-dependent manner. (A) Survival curves plotted from the LSM raw output after quality control of the plates and without manual annotation of the worm objects on the Worm Browser storyboard. (B) Survival curves plotted after manual annotation of the worm objects on the storyboard. (C) Survival curves plotted after manual annotation of the worm objects and death times on the storyboard. (D) Summary of all the objects detected by the LSM. The censored worm objects included objects excluded automatically (for example, due to worms moving outside of the scanned area) or manually by the experimenter (for example, due to worms bursting). (E) Tabular representation of the estimated mean lifespan after by-hand worm object annotation (left) and additional by-hand death time annotation (right). Percentage difference in the mean lifespan between neighboring groups of different auxin concentrations and the statistical power of detection. All graphs were plotted on the statistical software JMP. The data for this figure was adapted with permission from Oswal et al.23. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65462/65462fig8v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Moving beyond lifespan as a single end-point for studying aging, we then considered behavioral aging and investigated which steps of the post-experimental image analysis were most crucial for measuring it. Focusing on the relationship between vigorous movement cessation (VMC) and lifespan, we compared the LSM output at different stages of image analysis and validation (Figure 9A-D). We found that non-worm objects showed a unique relationship between the apparent VMC and lifespan, with the two being annotated as occurring nearly simultaneously in every object (Figure 9A). In contrast, true nematodes usually ceased moving vigorously several days prior to their death time (Figure 9A). This difference in the relationship between VMC and lifespan provides an additional means for rapidly identifying and excluding non-worm objects.
<img alt="Figure 9" class="xfigimg" src="/files/ftp_upload/65462/65462fig09.jpg" /> 
Figure 9: The effect of manual data validation on vigorous movement cessation (VMC) analysis.&#160;(A) Behavioral aging data without manual annotation of worm objects on the Worm Browser storyboard, displaying the relationship between the death times and VMC times in non-worm objects (in black) versus worm objects (in red). (B) Behavioral aging data plotted after manual annotation of worm objects on the storyboard. (C) Behavioral aging data plotted after manual annotation of worm objects and death times on the storyboard.(D) Tabular representation of the average remaining lifespan (ARL; obtained from the intercept between the death age and VMC age) across different steps of the image analysis pipeline and percentage difference in the ARL between worm object annotation and further death time annotation. (E) Graphic representation of the estimated ARLs across different steps of the post-image acquisition analysis, and their relationship to the worm lifespan (which is dependent on the auxin concentration: &#945;-napthaleneacetic acid). The spline fit was performed using a cubic spline method. All graphs were plotted on the statistical software JMP. The data for this figure was adapted with permission from Oswal et al.23. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65462/65462fig9v2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
We found that annotation and exclusion of non-worm objects using the Worm Browser was sufficient to provide a rough estimate of the relationship between the VMC and death times (Figure 9A-C), recapitulating the expected dynamics of the physiological decline in nematodes23. To further explore this, we considered the same RNA polymerase II data set and estimated the average remaining lifespan (ARL) after VMC for each subpopulation as the intercept of a linear regression model relating the lifespan to VMC. To understand the effect of data annotation on the ARL, we recalculated the ARL after each step of data annotation (Figure 9E). We found that the manual annotation of death times in behavioral aging analysis was especially important in longer-lived worms, in this instance, those exposed to the lowest concentrations of auxin (Figure 9D,E). In contrast to its minimal effect on the survival curves, manual annotation of the death times had a substantial effect on the quantitative relationship between the VMC and lifespan, increasing the estimated ARL, for instance at 0 &#181;M of auxin, from 8.09 days to 10.42 days after death time annotation; this represents an ARL difference of 29%. Therefore, we found that the relationship between VMC and death times explained by ARL was much more sensitive to by-hand annotation of the death times compared to measurements of death times for lifespan alone. This sensitivity can be explained by the relative durations of the ARL and the lifespan; the same adjustments to death time will usually be small relative to the lifespan but large relative to the ARL. Thus, adjustments to the death times will have a larger relative effect on the ARL compared to the lifespan.
Supplementary File 1: Structure of the Lifespan Machine experiment schedule file. The experiment schedule consists of three parts. First, the basic information about the experiment is included, such as the name and capture specification. From this part, generally only the name of the experiment needs to be changed for each new experiment. The second part of the document is generated by exporting scan areas from the Worm Browser and specifies the physical location of the scan areas for each scanner (&#34;asuna&#34;, &#34;bulma&#34;, &#34;moscow&#34;, &#34;rio&#34;, &#34;yuno&#34;, and &#34;yuki&#34; in this example). These are replaced for each new experiment and are copied from the TXT files generated for each scanner individually during step 4.1.2.3. The third part of the document provides information about the duration of the experiment, which should also be modified for each new experiment, and the capture frequency. The device will scan each defined area one at a time at specified intervals for the duration of the entire experiment. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/65462/Supplementary text 1.txt" target="_blank">Please click here to download this File.</a>

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

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

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.

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 ...

high-throughput-behavioral-aging-lifespan-assays-using-lifespan

Nanyang Technological University

Research

JoVE Journal

Biology

930 Views.  The Barcelona Institute of Science and Technology (BIST). 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.

Video: Author Spotlight: Automated Lifespan Monitoring – Discovering Aging Dynamics with the Lifespan Machine 

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages for identifying biologically active chemical structures that can modify complex phenotypes such as lifespan. Described here is a simple protocol for producing hundreds of 96-well culture plates with fairly consistent numbers of C. elegans in each well. Next, we specified how to use these cultures to screen thousands of chemicals for effects on the lifespan of the nematode C. elegans. This protocol makes use of temperature sensitive sterile strains, agar plate conditions, and simple animal handling to facilitate the rapid and high throughput production of synchronized animal cultures for screening.

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

Here we describe a simple protocol for rapidly producing hundreds of nematode growth media agar, 96-well culture plates with consistent numbers of Caenorhhabditis elegans per well. These cultures are useful for the phenotypic screening of whole organisms. We focus here on using these cultures to screen chemicals for pro-longevity effects.

Caenorhabditis elegans is a useful organism for testing chemical effects on physiology. Whole organism small molecule screens offer significant advantages ...

Biochemistry

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus allowing a single investigator to screen more treatments or conditions over the same amount of time without loss of data quality. The method requires common equipment found in most laboratories working with C. elegans and is thus simple to adopt. The approach centers on assaying independent samples of a population at each observation point, rather than a single sample over time as with traditional longitudinal methods. Scoring entails adding liquid to the wells of a multi-well plate, which stimulates C. elegans to move and facilitates quantifying changes in healthspan. Other major benefits of the Replica Set method include reduced exposure of agar surfaces to airborne contaminants (e.g. mold or fungus), minimal handling of animals, and robustness to sporadic mis-scoring (such as calling an animal as dead when it is still alive). To appropriately analyze and visualize the data from a Replica Set style experiment, a custom software tool was also developed. Current capabilities of the software include plotting of survival curves for both Replica Set and traditional (Kaplan-Meier) experiments, as well as statistical analysis for Replica Set. The protocols provided here describe the traditional experimental approach and the Replica Set method, as well as an overview of the corresponding data analysis.

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Here we describe the Replica Set method, an approach to quantitatively measure C. elegans lifespan/survival and healthspan in a high-throughput and robust manner, thus allowing screening of many conditions without sacrificing data quality. This protocol details the strategy and provides a software tool for analysis of Replica Set data.

The Replica Set method is an approach to quantitatively measure lifespan or survival of Caenorhabditis elegans nematodes in a high-throughput manner, thus ...

Genetics

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive decline, such as Alzheimer&#39;s disease (AD), have become a promising tool to elucidate the underlying mechanisms and promote the therapeutic advances. An important step is the validation and characterization of expected behavioral abnormality in the models, in the case of AD, cognitive decline. The long-term behavioral investigations of laboratory animals to study the effect of aging demand substantial efforts from researchers. The IntelliCage system is a high-throughput and cost-effective test battery for mice that eliminates the need for daily human handling. Here, we describe how the system is utilized in the long-term phenotyping of a genetic Alzheimer&#39;s disease model, specifically focusing on the cognitive functions. The experiment employs repeated battery of tests that assess spatial learning and executive functions. This cost-effective age-dependent phenotyping allows us to identify the transient and/or permanent effects of genes on various cognitive aspects.

This paper describes a protocol for cognitive assessments for genetic models of the Alzheimer&#39;s disease using the IntelliCage system, which is a high throughput automated behavioral monitoring system with operant conditioning.

Multiple factors&#8212;such as aging and genes&#8212;are frequently associated with cognitive decline. Genetically modified mouse models of cognitive ...

Behavior

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and chronological life spans are two established paradigms used to study aging in yeast. Replicative aging is defined as the number of daughter cells a single yeast mother cell produces before senescence; chronological aging is defined by the length of time cells can survive in a non-dividing, quiescence-like state 2. We have developed a high-throughput method for quantitative measurement of chronological life span. This method involves aging the cells in a defined medium under agitation and at constant temperature. At each age-point, a sub-population of cells is removed from the aging culture and inoculated into rich growth medium. A high-resolution growth curve is then obtained for this sub-population of aged cells using a Bioscreen C MBR machine. An algorithm is then applied to determine the relative proportion of viable cells in each sub-population based on the growth kinetics at each age-point. This method requires substantially less time and resources compared to other chronological lifespan assays while maintaining reproducibility and precision. The high-throughput nature of this assay should allow for large-scale genetic and chemical screens to identify novel longevity modifiers for further testing in more complex organisms.

Chronological aging in yeast refers to the loss of cell viability associated with time in stationary phase. Here we describe a high-throughput method for quantitatively determining yeast chronological life span.

The budding yeast Saccharomyces cerevisiae has proven to be an important model organism in the field of aging research 1. The replicative and ...

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans

The discovery and development of Caenorhabditis elegans as a model organism was influential in biology, particularly in the field of aging. Many historical and contemporary studies have identified thousands of lifespan-altering paradigms, including genetic mutations, transgenic gene expression, and hormesis, a beneficial, low-grade exposure to stress. With its many advantages, including a short lifespan, easy and low-cost maintenance, and fully sequenced genome with homology to almost two-thirds of all human genes, C. elegans has quickly been adopted as an outstanding model for stress and aging biology. Here, several standardized methods are surveyed for measuring lifespan and healthspan that can be easily adapted into almost any research environment, especially those with limited equipment and funds. The incredible utility of C. elegans is featured, highlighting the capacity to perform powerful genetic analyses in aging biology without the necessity of extensive infrastructure. Finally, the limitations of each analysis and alternative approaches are discussed for consideration.

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans

Caenorhabditis elegans serve as an excellent model system with robust and low-cost methods for surveying healthspan, lifespan, and resilience to stress.

The discovery and development of Caenorhabditis elegans as a model organism was influential in biology, particularly in the field of aging. Many ...

Measuring Caenorhabditis elegans Life Span in 96 Well Microtiter Plates

Lifespan is a biological process regulated by several genetic pathways. One strategy to investigate the biology of aging is to study animals that harbor mutations in components of age-regulatory pathways. If these mutations perturb the function of the age-regulatory pathway and therefore alter the lifespan of the entire organism, they provide important mechanistic insights1-3.
Another strategy to investigate the regulation of lifespan is to use small molecules to perturb age-regulatory pathways. To date, a number of molecules are known to extend lifespan in various model organisms and are used as tools to study the biology of aging4-16. The number of molecules identified thus far is small compared to the genetic "toolset" that is available to study the biology of aging.
Caenorhabditis elegans is one of the principle models used to study aging because of its excellent genetics and short lifespan of three weeks. More recently, C.elegans has emerged as a model organism for phenotype based drug screens5,7,16-20 because of its small size and its ability to grow in microtiter plates.
Here we present an assay to measure C.elegans lifespan in 96 well microtiter plates. The assay was developed and successfully used to screen large libraries for molecules that extend C.elegans lifespan7. The reliability of the assay was evaluated in multiple tests: first, by measuring the lifespan of wild type animals grown at different temperatures; second, by measuring the lifespan of mutants with altered lifespans; third, by measuring changes in lifespan in response to different concentrations of the antidepressant Mirtazepine. Mirtazepine has previously been shown to extend lifespan in C.elegans7. The results of these tests show that the assay is able to replicate previous findings from other assays and is quantitative. The microtiter format also makes this lifespan assay compatible with automated liquid handling systems and allows integration into automated platforms.

Measuring Caenorhabditis elegans Life Span in 96 Well Microtiter Plates

In this protocol we present a method to measure Caenorhabditis elegans lifespan in 96 well microtiter plates.

Lifespan is a biological process regulated by several genetic pathways. One strategy to investigate the biology of aging is to study animals that harbor ...

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Studies using the Saccharomyces cerevisiae aging model have uncovered life span regulatory pathways that are partially conserved in higher eukaryotes1-2. The simplicity and power of the yeast aging model can also be explored to study DNA damage and genome maintenance as well as their contributions to diseases during aging. Here, we describe a system to study age-dependent DNA mutations, including base substitutions, frame-shift mutations, gross chromosomal rearrangements, and homologous/homeologous recombination, as well as nuclear DNA repair activity by combining the yeast chronological life span with simple DNA damage and mutation assays. The methods described here should facilitate the identification of genes/pathways that regulate genomic instability and the mechanisms that underlie age-dependent DNA mutations and cancer in mammals.

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

Here we describe a set of DNA mutation assays that can be combined with the yeast chronological life span model to study the genes/pathways that regulate or contribute to genomic DNA instability during aging.

Studies using the Saccharomyces cerevisiae aging model have uncovered life span regulatory pathways that are partially conserved in higher eukaryotes1-2. ...

High-throughput Crystallization of Membrane Proteins Using the Lipidic Bicelle Method

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane bilayer that surrounds all cells and organelles. Alterations in the function of MPs result in many human diseases and disorders; thus, an intricate understanding of their structures remains a critical objective for biological research. However, structure determination of MPs remains a significant challenge often stemming from their hydrophobicity. 
MPs have substantial hydrophobic regions embedded within the bilayer. Detergents are frequently used to solubilize these proteins from the bilayer generating a protein-detergent micelle that can then be manipulated in a similar manner as soluble proteins. Traditionally, crystallization trials proceed using a protein-detergent mixture, but they often resist crystallization or produce crystals of poor quality. These problems arise due to the detergent&prime;s inability to adequately mimic the bilayer resulting in poor stability and heterogeneity. In addition, the detergent shields the hydrophobic surface of the MP reducing the surface area available for crystal contacts. To circumvent these drawbacks MPs can be crystallized in lipidic media, which more closely simulates their endogenous environment, and has recently become a de novo technique for MP crystallization.
Lipidic cubic phase (LCP) is a three-dimensional lipid bilayer penetrated by an interconnected system of aqueous channels1. Although monoolein is the lipid of choice, related lipids such as monopalmitolein and monovaccenin have also been used to make LCP2. MPs are incorporated into the LCP where they diffuse in three dimensions and feed crystal nuclei. A great advantage of the LCP is that the protein remains in a more native environment, but the method has a number of technical disadvantages including high viscosity (requiring specialized apparatuses) and difficulties in crystal visualization and manipulation3,4. Because of these technical difficulties, we utilized another lipidic medium for crystallization-bicelles5,6 (Figure 1). Bicelles are lipid/amphiphile mixtures formed by blending a phosphatidylcholine lipid (DMPC) with an amphiphile (CHAPSO) or a short-chain lipid (DHPC). Within each bicelle disc, the lipid molecules generate a bilayer while the amphiphile molecules line the apolar edges providing beneficial properties of both bilayers and detergents. Importantly, below their transition temperature, protein-bicelle mixtures have a reduced viscosity and are manipulated in a similar manner as detergent-solubilized MPs, making bicelles compatible with crystallization robots. 
Bicelles have been successfully used to crystallize several membrane proteins5,7-11 (Table 1). This growing collection of proteins demonstrates the versatility of bicelles for crystallizing both alpha helical and beta sheet MPs from prokaryotic and eukaryotic sources. Because of these successes and the simplicity of high-throughput implementation, bicelles should be part of every membrane protein crystallographer&prime;s arsenal. In this video, we describe the bicelle methodology and provide a step-by-step protocol for setting up high-throughput crystallization trials of purified MPs using standard robotics.

Bicelles are lipid/amphiphile mixtures that maintain membrane proteins (MPs) within a lipid bilayer but have unique phase behavior that facilitates high-throughput screening by crystallization robots. This technique has successfully produced a number of high-resolution structures from both prokaryotic and eukaryotic sources. This video describes protocols for generating the lipidic bicelle mixture, incorporating MPs into the bicelle mixture, setting up crystallizations trials (manually as well as robotically) and harvesting crystals from the medium.

Membrane proteins (MPs) play a critical role in many physiological processes such as pumping specific molecules across the otherwise impermeable membrane ...

Measurement of Lifespan in Drosophila melanogaster

Aging is a phenomenon that results in steady physiological deterioration in nearly all organisms in which it has been examined, leading to reduced physical performance and increased risk of disease. Individual aging is manifest at the population level as an increase in age-dependent mortality, which is often measured in the laboratory by observing lifespan in large cohorts of age-matched individuals. Experiments that seek to quantify the extent to which genetic or environmental manipulations impact lifespan in simple model organisms have been remarkably successful for understanding the aspects of aging that are conserved across taxa and for inspiring new strategies for extending lifespan and preventing age-associated disease in mammals.
The vinegar fly, Drosophila melanogaster, is an attractive model organism for studying the mechanisms of aging due to its relatively short lifespan, convenient husbandry, and facile genetics. However, demographic measures of aging, including age-specific survival and mortality, are extraordinarily susceptible to even minor variations in experimental design and environment, and the maintenance of strict laboratory practices for the duration of aging experiments is required. These considerations, together with the need to practice careful control of genetic background, are essential for generating robust measurements. Indeed, there are many notable controversies surrounding inference from longevity experiments in yeast, worms, flies and mice that have been traced to environmental or genetic artifacts1-4. In this protocol, we describe a set of procedures that have been optimized over many years of measuring longevity in Drosophila using laboratory vials. We also describe the use of the dLife software, which was developed by our laboratory and is available for download (<a href="http://sitemaker.umich.edu/pletcherlab/software">http://sitemaker.umich.edu/pletcherlab/software</a>). dLife accelerates throughput and promotes good practices by incorporating optimal experimental design, simplifying fly handling and data collection, and standardizing data analysis. We will also discuss the many potential pitfalls in the design, collection, and interpretation of lifespan data, and we provide steps to avoid these dangers.

Measurement of Lifespan in Drosophila melanogaster

Drosophila melanogaster is a powerful model organism for exploring the molecular basis of longevity regulation. This protocol will discuss the steps involved in generating a reproducible, population-based measurement of longevity as well as potential pitfalls and how to avoid them.

Aging is a phenomenon that results in steady physiological deterioration in nearly all organisms in which it has been examined, leading to reduced ...

Invertebrate Lifespan Quantification

Many animals naturally stop growing upon reaching adulthood, after which they undergo aging or "senescence" until dying. The amount of time between an organism\'s birth and death is called its lifespan, which can be influenced by various biological and environmental factors. By exposing organisms to different growth conditions, scientists can better understand the factors affecting lifespan. Flies and worms are ideal organisms to perform such experiments, given their short generation time and simple culture requirements.
This video provides a brief overview of the factors affecting aging, and goes on to describe basic protocols for invertebrate lifespan quantification experiments. Finally, three research applications of lifespan quantification will be discussed. These experiments explore the effects of diverse factors, such as temperature, drugs, pathogens, and diet, on lifespan.

Lifespan Quantification in Drosophila and C.elegans

Many animals naturally stop growing upon reaching adulthood, after which they undergo aging or "senescence" until dying. The amount of time between an ...

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

Summary

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Chapters in this video

A Simple Method for High Throughput Chemical Screening in Caenorhabditis Elegans

The Replica Set Method: A High-throughput Approach to Quantitatively Measure Caenorhabditis elegans Lifespan

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage

Quantifying Yeast Chronological Life Span by Outgrowth of Aged Cells

Surveying Low-Cost Methods to Measure Lifespan and Healthspan in Caenorhabditis elegans

Measuring Caenorhabditis elegans Life Span in 96 Well Microtiter Plates

Studying Age-dependent Genomic Instability using the S. cerevisiae Chronological Lifespan Model

High-throughput Crystallization of Membrane Proteins Using the Lipidic Bicelle Method

Measurement of Lifespan in Drosophila melanogaster

Invertebrate Lifespan Quantification