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 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 (http://sitemaker.umich.edu/pletcherlab/software). 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.
We recommend storing experimental foods, yeast paste, and grape agar plates that appear in the protocol at 4 °C and using them within 1-2 months as long as mold and dryness have not set in. Standard environmental conditions for both the larval and adult stage involve maintenance of flies in an incubator at 25 °C with a 12:12 hr light dark cycle and 60% relative humidity.
1. Preparation of Experimental Food
2. Preparation of a Live Yeast Paste
Combine 5-6 ml of water with 3 g of active dry yeast and mix well. The consistency of the yeast paste should be that of a smooth peanut butter.
3. Preparation of a Grape Agar Plate
4. Collection of Synchronized Eggs
All media used in this protocol, i.e. yeast, grape agar plates, CT and 10% SY food, should be at room temperature.
5. Collection of Age-matched Adult Flies
6. Sorting Flies and Setting up Longevity Experiment
7. Setting Up the Excel spreadsheet to Track the Longevity Experiment
8. Maintaining the Longevity Experiment
The vials containing fresh food should be at room temperature for each transfer.
9. Data Analysis
A simplified scheme of the protocol is presented in Figure 1, where key steps are outlined. The synchronization part of the protocol can be used for various assays that require age-matched adult flies.
Typical survivorship curves of wild-type flies are shown in Figure 2a, using the dLife experiment management software (Figure 2b,c). Adult males usually live shorter, with both populations achieving a mean and median longevity of >50 days on a 10% SY food at 25 °C. Note that survivorship remains high in the early part of the experiment and then declines exponentially.
Drosophila lifespan is affected by environmental conditions, such as temperature and diet. Figure 3a shows that adult males typically live markedly shorter as temperature is increased. Likewise, the effect of diet on lifespan is presented in Figure 3b: Adult female flies on a less concentrated diet (5% SY) typically live significantly longer than those on a more concentrated diet (15% SY).
The density of cohorts during development can influence adult lifespan and alter developmental timing. Here we show an example of how different densities of synchronized eggs affect larval development. As shown in Figure 4, adult fly yield is poor and the food surface is susceptible to drying when the number of eggs is too low. On the other end of the spectrum, larval development is retarded in over-crowded bottles, and the yield of adult flies is reduced.
The survivorship curve of the cohort as a whole can be influenced significantly by anomalous vial effects, as shown in Figure 5. Irregular survival data for individual vials may have several causes, such as poor food quality or bacterial/fungal accumulation and infection. While such anomalous deaths can skew the population survivorship measure, there is not a straightforward metric for appropriately determining that a vial should be excluded from the experiment. These situations are therefore best avoided by good handling practices and mitigated by using a large sample size.
Figure 6 shows examples of vial conditions that can lead to anomalous deaths. In general, any condition that can lead to small crevasses where flies may get stuck and die should be avoided. Examples include: bubbles in the food, dryness in the food that leads to shrinking away from the vial wall, and cracking in the food are shown in Figure 5a (a mild example with a single bubble), Figure 5b, and Figure 5c, respectively. Excessively dry food, as shown in Figure 5b and Figure 5c should be carefully flipped during transfers, as the food can dislodge and collapse onto the flies in the new vial. Bacterial growth on the surface of the food can lead to infection or physical entrapment and thus may increase mortality. Some bacteria on the surface of the food appear transparent and shiny as if there is perspiration from the food (not shown), while other types of bacteria will manifest as white colonies (Figure 5d). Vials exhibiting any of these conditions should be noted and further considered when the data are interpreted. In general, we emphasize that careful attention to husbandry in both the larval and adult stages can support the longevity and health of adult flies and reduce the occurrence of problems leading to ambiguous causes of death in later life.
Figure 1. Simplified schematic of a Drosophila lifespan assay.
Figure 2. (A) Representative lifespan curves of w1118 control female (circles) and male (squares) adult flies at 25 °C on a SY10% food. (B,C) Representative screen shots of the dLife software.
Figure 3. Effects of temperature (A) and diet (B) on adult lifespan. A. Adult control (Canton S) male flies were maintained through adulthood at 18 °C, 25 °C, or 29 °C. B. Adult control (w1118) female flies were exposed to either a 15% SY or 5% SY diet.
Figure 4. Day 9 of development (at 25 °C) of synchronized eggs, aliquoted in CT food bottles. Volume of the embryo-containing aliquot is as shown beneath each bottle.
Figure 5. Representative plot from the dLife software showing survivorship by vial. The arrow indicates a single anomalous vial within a group.
Figure 6. Examples of suboptimal food quality. A. Bubbles on the food surface. B. Food shrunk away from the edge of the vial. C. Cracks in the food. D. Bacteria accumulation on the surface of the food.
The protocol presented here describes a method for producing reproducible measurements of adult longevity in Drosophila that is adaptable for assessment of genetic, pharmacological, and environmental interventions. Crucial aspects of the protocol include carefully controlling the larval development environment, minimizing adult stress, and minimizing bias across experimental groups and controls. We also present the use of the dLife lifespan experiment management software. By simply attaching a bar code or RFID tag to each vial, the dLife program will assist in data acquisition for each measurement and in plotting the survivorship curve. While it is currently most suitable for studies of fly lifespan using vials, this experiment management tool could be easily adapted for use in other organisms, with different types of population chambers, or for additional measures of survival, including stress resistance and drug toxicity.
Prior to commencing any longevity assessment, one must first carefully control the production of parental stocks. Genetic variability is an important factor, as is the health of parental strains. These factors have led to considerable public controversy associated with early studies that reported putative longevity regulators13. The researcher has several options to minimize effects attributable to variability in genetic background. For genetic manipulations, all genetically altered strains should be background-controlled through either backcrossing to a control strain (at least 6 times) or using inducible systems (e.g., temperature sensitive alleles or drug-inducible transgene expression). The GeneSwitch and Tet-on systems14,15 are popular and allow direct comparison of flies with the same genetic background in which the transgene is either induced or not induced. For all inducible systems, appropriate contemporaneous controls are required to avoid confounds associated with the inducer. Failure to control for genetic background nearly always results in hybrid vigor, which can extend lifespan in the F1 generation of a cross between two different strains for reasons unrelated to the intended manipulation 13. This factor is of particular concern when using the GAL4-UAS system. For environmental interventions (e.g., drug treatment, diet, temperature, etc.), it is advisable to study the effects of the intervention on more than one strain. Finally, both parental age16 and stress17 can influence the longevity of the F1 generation, and for this reason, healthy young adults should be chosen for egg production.
Important aspects of controlling the larval/pupal environment include the prevention of overcrowding and the maintenance of a controlled environment with strict regulation of light-dark periods, humidity, and temperature. These factors will affect both the timing of development and the physical quality of the resulting adults. Adverse larval environments, such as high larval density, may lead to activation of stress-inducible factors (e.g., heat shock protein expression) that are known to influence adult longevity18.
During the adult phase, careful attention to the environment remains essential. The choice of diet alone can greatly influence lifespan and can interact with genetic factors to produce diet-specific effects on longevity. Furthermore, some common food base alternatives (yeast extract instead of lyophilized whole brewer’s yeast) can dramatically shorten lifespan19, leaving open the possibility that the food itself is causing organismal stress that may impair the assessment of longevity. In this article, we have highlighted potential sources of stress in the dietary environment including physical anomalies in the food (e.g., bubbles, foam, cracks, bacteria, etc.) that can physically entrap the animals. Regular replacement of old food with fresh food (at least three times each week) can overcome many of these difficulties. Furthermore, temperature20, humidity (personal observations), lighting21, and the presence of conspecifics (social environment)22 can all modulate lifespan, and attention to controlling these factors within the experiment is important to avoid bias in the results. While the number of flies within a vial decreases with time, we have found that the use of anesthesia to adjust the number of flies can increase mortality in an age-dependent manner (Pletcher, personal observations), and we do not recommended this procedure. The precise position of the vials in an incubator is also a factor, even in a seemingly controlled environment. Randomized physical distribution of experimental groups with controls can mitigate bias associated with vial placement and is required for proper statistical inference. Even with carefully controlled conditions, slight differences can be observed between experiments and the use of within-experiment controls is essential.
Alternative feeding approaches have been proposed for lifespan analysis including a capillary feeder approach (CAFE method)23. This method excels in the ability to provide precise measurements of food consumption, but it results in markedly short-lived flies24. Potential stresses associated with the feeding environment must be considered when assessing the combined relationship between diet and genetic factors in overall longevity.
Demographic analysis, including the calculation of survivorship and mortality curves can reveal much information about the dynamics of the aging population. A typical survivorship curve will remain relatively flat for a long period early in life and increase its rate of decline at older ages, which corresponds to a period of low mortality followed by a period of an exponential increase in mortality. A stressful environment will usually manifest as an excess of early deaths in the population and an anomalous dip in the survivorship curve. While such a result may indicate significant differences between treatments, it will not normally be robust to replication. We therefore recommend at least two independent (i.e., non-contemporaneous) replicate experiments be executed before any firm conclusion are drawn. It may be that additional effort toward increasing the sample size, controlling the husbandry conditions, and improving the health of parental stocks is required.
Right-censoring (removing animals from the experiment that escape or are presumed dead from accidental causes) of animals that die from stressful environmental conditions should be applied with extreme caution. Strictly speaking, censoring must occur randomly across experimental treatments, and if the experimental intervention modulates stress sensitivity, one could inadvertently apply treatment-level selection to the population. As a general rule, avoiding the presence of factors that could produce ambiguous early death (primarily associated with the food source) is better than censoring, and censoring should only be applied to organisms that were observed to die or escape during physical handling.
A final consideration is the assessment of statistical significance. While large cohort sample sizes provide impressive power to distinguish small differences between treatments, the potential biological significance of such a difference must also be considered. With reasonably sized longevity experiments, differences as low as 1-2% are often highly statistically significant, but the overall impact of the intervention on health status may be minor. Therefore, both statistical and biological significance must be considered when interpreting the overall results of the experiment. Inference about the aging process from survival experiments can be augmented by measures of age-related declines in behavioral or physiological health measures, including climbing ability25 and gastrointestinal wall integrity7.
In summary, the Drosophila model organism an appealing choice for studying mechanisms of aging. With careful experimental technique, robust demographic analysis can provide insight into the impact of pharmacological and genetic factors on the aging process.
The authors have nothing to disclose.
This work was supported by funding from the Ellison Medical Foundation (SDP, http://www.ellisonfoundation.org/index.jsp), NIH K01AG031917 (NJL, http://www.nih.gov/), NIH 5T32GM007315-35 (JR) and NIH R01AG030593 (SDP). This work utilized the resources of the Drosophila Aging Core (DAC) of the Nathan Shock Center of Excellence in the Biology of Aging funded by the National Institute of Aging P30-AG-013283 (http://www.nih.gov/). The authors would like to thank the Pletcher Laboratory for helpful discussions and particularly Brian Chung for critical reading of the manuscript. We would like to acknowledge Nick Asher and Kathryn Borowicz for assistance with data collection.
Name of the reagent | Company | Catalogue number | Comments (optional) |
Active Dry Yeast | Fleishmann’s Yeast | 2192 | |
Grape Agar Powder Premix | Genesee Scientific | 47-102 | |
Large Embryo Collection Cages | Genesee Scientific | 59-101 | |
Large Replacement End Caps | Genesee Scientific | 59-103 | |
6 oz Square Bottom Bottles, polypropylene | Genesee Scientific | 32-130 | |
Flugs Closures for Stock Bottles | Genesee Scientific | 49-100 | |
Drosophila Vials, Wide, Polystrene | Genesee Scientific | 32-117 | |
Flugs Closures for Wide Vials | Genesee Scientific | 49-101 | |
Wide Orifice Aardvark Pipet Tips, 200 ul | Denville Scientific | P1105-CP | |
Flystuff Flypad, Standard Size | Genesee Scientific | 59-114 | |
BD Falcon 15 ml Conical Centrifuge Tubes | Fisher Scientific | 14-959-70C | |
Fisherbrand Petri Dishes with Clear Lids, Raised Ridge; 100 O.D. x 15 mm H; | Fisher Scientific | 08-757-12 | |
Kimax* Colorware Flasks 1,000 ml yellow | Fisher Scientific | 10-200-47 | |
PBS pH 7.4 10x | Invitrogen | 70011044 | |
Gelidium Agar | Mooragar | n/a | |
Brewer’s Yeast | MP Biomedicals | 0290331280 | |
Granulated Sugar | Kroger | n/a | |
Tegosept | Genesee Scientific | 20-266 | Fly Food Preservative |
Propionic Acid, 99% | Acros Organics | 149300025 | Fly Food Preservative |
Kanamycin Sulfate | ISC BioExpress | 0408-10G | |
Tetracycline HCl | VWR | 80058-724 |