Biological research can, at times, be mind-numbingly tedious: scientists often have to do the same experiment over and over on many different samples. When working with animals such as fruit flies, this means researchers have to physically handle large numbers of specimens, selecting certain individuals or moving them from one container to another to perform the study. This represents a serious bottleneck that slows down discovery.

Automation represents an obvious solution to this issue. In fact, it has already revolutionized fields like molecular biology, where robots can handle the liquids required for the experiments. Yet, it is not so easy to automate tasks that involve animals larger than a millimeter. To fill that gap, Alisch et al. have developed a robotic system called Modular Automated Platform for Large-scale Experiments (MAPLE) that can manipulate fruit flies and other small organisms.

Using gentle vacuum, MAPLE can pick up individual flies to move them from one compartment to another. These areas could be places where the insects grow or where experimental measurements are automatically gathered. Putting the robot to work, Alisch et al. used MAPLE to collect virgin female flies for genetic experiments, a common task in fruit flies laboratories. The system was also configured to load flies into arenas where their behavior could be measured. Finally, MAPLE assisted with an experiment that involved tracking the interactions of known individuals to examine if the flies exhibited social networks, and if those networks were stable. This logistically complicated experiment would have been difficult to run without the help of an automated system. Alisch et al. also show that the robot can be adjusted to work with various species often used for research, such as nematode worms, yeast, slime mould and even bumblebees. This allows the system to be useful in a range of research fields.

As MAPLE fits on a table top and is fairly affordable, the hope is that it could help many scientists do their experiments faster and with greater consistency, freeing up time for creative thinking and new ideas. Ultimately, this tool could help to speed up scientific progress.

Genetic model organisms are used to advance our biological understanding in numerous areas including disease and its treatment, basic cell biology, neuroscience and behavior. Species like Saccharomyces cerevisiae, Caenorhabditis elegans, and Drosophila melanogaster are desirable lab model organisms due to their rapid reproduction, ease of rearing, and especially their deep genetic toolkits comprising strains with varying genotypes and transgenic alterations that permit rapid, mechanistic inquiries. To take advantage of these toolkits, screen experiments quantify the phenotypes of hundreds (Vitaterna et al., 1994), thousands (Kain et al., 2012), tens of thousands (Ayroles et al., 2015; Buchanan et al., 2015; Churgin et al., 2017) or even hundreds of thousands of individual animals (Robie et al., 2017). With the ongoing improvement and widespread adoption of high-performance machine vision phenotyping (Branson et al., 2009; Dankert et al., 2009; Kabra et al., 2013; Kimura et al., 2014), the time needed to manually handle experimental animals remains the bottleneck limiting data collection.

The advent of liquid-handling robots has radically changed the face of molecular biology, enabling techniques and experiments that had long been imagined, but were too complex, lengthy, or tedious to have been previously realized. But there are no comparable systems for handling of experimental organisms that are larger than ~1 mm or cannot be suspended in liquid. There are large-scale systems that handle adult flies in vials, in the form of ‘fly flipping’ robots. However, these take up a whole room, generally in a core facility, and cost hundreds of thousands of dollars to purchase and maintain, and are therefore inaccessible to most labs. And while there are many examples of high-throughput phenotypic assays in Drosophila (Branson et al., 2009; Kabra et al., 2013; Kain et al., 2012; Buchanan et al., 2015; Geissmann et al., 2017), the systems that are not single-purpose still require human intervention to load and unload individual flies. Some researchers have used flies' natural tendency to climb up (negative gravitaxis) to isolate individuals for behavioral analysis (von Reyn et al., 2014), imaging (Medici et al., 2015), or microsurgery (Savall et al., 2015). But dependence on this particular behavior fundamentally caps throughput, inadvertently selects for a subset of a population, and limits eligible genotypes. An alternative approach, actively conveying flies with airflow (MacMillan and Hughson, 2014) permits moving animals on demand, and opens the door for increased throughput. The dearth of instruments for automating Drosophila experiments is representative of the situation for many other lab organisms, such as yeast and C. elegans, where there is no standard platform for automated handling of the organisms themselves.

Here, we present an automated platform that is high-throughput and flexible enough to assist in conducting diverse experimental protocols in Drosophila and other species. Due to its modular design, the system can automate diverse assays in a wide variety of organisms (including yeast, C. elegans, the slime mold Physarum polycephalum, and the bumblebee Bombus impatiens, an assortment chosen to demonstrate the platform’s versatility). For fruit flies, where we developed significant capabilities, this instrument can conduct numerous protocols, including loading of individual fruit flies for circadian rhythm (Pfeiffenberger et al., 2010; Geissmann et al., 2017) experiments, or aiding with lab chores like collecting virgin female flies for genetic crosses or passaging individual flies in controlled culture conditions for longevity assays. The physical platform of this instrument integrates animal husbandry and phenotyping, permitting end-to-end experimental protocols. Its low cost (~$3,500), programmability, and scalability permit large-scale experiments that take advantage of the many benefits lab model organisms offer, such as huge fly genetic libraries containing thousands of lines (Jenett et al., 2012; Thibault et al., 2004). After demonstrating MAPLE’s breadth of utility across species, we highlight the depth of its capabilities with two particularly time- and manual labor-intensive fruit fly tasks: rapidly collecting virgin females, and large-scale longitudinal measurement of fly social networks and behavior. The latter experiment reveals previously unknown stability of Drosophila social interactions, and confirms that both olfaction and vision are required for dyad-specific social interactions in this context.

We developed MAPLE, a modular, automated platform for large-scale experiments, to expand automated experimental capabilities for lab model organisms (Figure 1). By design, MAPLE is versatile, scalable, and relatively inexpensive, with all components of the core fly-handling robot costing roughly $3500. We displayed the versatility of this system by adapting the MAPLE platform to detect, place, and streak out individual S. cerevisiae colonies (Figure 1B), autonomously record C. elegans behavior (Figure 1C) and P. polycephalum movement (Figure 1D), as well as automatically monitor the behavior of multiple bumblebee colonies over long periods (Figure 1F,G). We have made the design and code for MAPLE open access in the hope that it, or descendant approaches, will be adopted by our field.

After demonstrating that MAPLE did not discernibly alter the baseline behavior of animals compared to manual handling (Figure 2), we developed a procedure by which MAPLE could help conduct a repetitive lab chore that nearly all Drosophila biologists are familiar with — collecting virgin females for genetic crosses. Using a Fly Dispenser, MAPLE collected individual animals as they eclosed from the pupal case and kept them in isolation, ensuring their virginity (Figure 4). To arrive at a final collection of virgin females, these singly-housed individuals were sexed by hand. Even with this manual step, virgins were procured at a much higher (~3 x) rate with MAPLE’s assistance than without it (measured in terms of human labor time per virgin female).

MAPLE’s capacity to handle individual flies in combinatorially complex experimental designs (Figure 3), allowed us to demonstrate that dyadic fly social behavior relies on both visual and olfactory cues. Dependence of social behavior on visual (Mery et al., 2009; Simon et al., 2012) and olfactory cues (Schneider et al., 2012; Billeter and Levine, 2015) is known from experiments observing many flies simultaneously. We further found that individual differences in dyadic social behavior — as well as derived social interaction network topography — remain stable over days (Figure 5). Stability of social networks on short timescales (Schneider et al., 2012) is known, but long-term stability of social networks is a novel finding. This illustrates that MAPLE can (1) replicate known results without introducing significant behavioral confounds and (2) extend our knowledge by conducting complex, long-term experiments that are challenging for human experimentalists.

Technologies that automate experiments will particularly facilitate the phenotyping of individual animals, an approach that reveals underappreciated intragenotypic variability. Our group has shown that individual flies generally exhibit very different behaviors from one another even if they are reared in the same environment and have the same genotype, and that these differences persist across days (Kain et al., 2012; Buchanan et al., 2015; Ayroles et al., 2015; Kain et al., 2015; Todd et al., 2017). MAPLE allowed us to show that this extends to dyad-specific social interactions in flies (Figure 5H–J). To our knowledge, this is the first demonstration that dyad-specific social interactions are persistent on days-long timescales in flies.

As the behavioral assay used here relies on physically separating individual flies, it is limited to examining visual and olfactory aspects of social interactions. To describe persistence of dyad-specific social interactions in flies more completely, further investigation including somatosensory and gustatory information exchange is necessary (Krstic et al., 2009; Schneider et al., 2012; Ramdya et al., 2015). Moreover, this assay presents flies with a choice to interact with a particular partner, or not. Assays with groups of flies, where the alternative to interacting with a particular fly includes interacting with potentially many other flies, may produce different results (Ramdya et al., 2017), although interaction networks in a group context are at least partially stable over the course of experiments (Schneider et al., 2012).

MAPLE is designed to be a general purpose instrument, therefore it has some disadvantages compared to purpose-built devices that perform a single task. For example, while MAPLE’s camera can be used to acquire time-lapse movies of yeast colony growth, Physarum plasmodium movement, or C. elegans locomotion, purpose-built worm imaging systems can provide comparable data with higher throughput (Churgin et al., 2017) or higher resolution (Stern et al., 2017). Likewise, purpose-built yeast colony picking robots (e.g. BioMatrix, S and P Robotics) achieve higher performance on specific tasks like transferring colonies, although typically at a higher price. MAPLE’s open, modular design is meant to facilitate customization and prototyping, so it may be a good platform for the development of new organism-handling hardware, for example, for the dissection and transfer of Physarum syncytium or individual worms. These and other experimental applications might rely on liquid-handling. MAPLE currently has no liquid-handling capabilities, but there are open source liquid-handling robotic systems such as Opentrons (http://opentrons.com) which could be integrated with MAPLE. Specifically, liquid-handling effectors could be installed on MAPLE z-axes, replacing some or all of the current effectors.

In its current configuration, MAPLE has some drawbacks. Its effectors move slightly slower than the hands of a trained experimentalist, so its advantages derive from it not growing tired or losing focus. MAPLE does not execute every action with 100% success. For example, attempts to remove an awake fly from a behavioral arena, using vacuum through a loading port, succeed approximately 90% of the time. Given this error rate, the experimental procedures that include removing flies from arenas use the camera to inspect the arena after each attempt to confirm if the fly has been successfully removed. Similarly, our procedure to transfer yeast colonies used the collision-detector to confirm that the object manipulator had successfully removed petri dish lids. These checks reduce the efficiency of MAPLE-conducted experiments (particularly compared to manual experiments), but mean that the operator can walk away from the device confident that MAPLE will eventually get it right. Given its redundancies and fail-safes, MAPLE can work autonomously for long periods (e.g. 12 hr for the Physarum experiment) without getting stuck.

A potential advantage of using MAPLE for fly experiments specifically is the option to avoid anaesthesia. The Fly Dispenser releases awake animals into the MAPLE system, and these can then be moved between the FlyPlate and behavioral arenas using the multi-position loading ports we developed. Avoiding anesthesia has multiple benefits, including reducing the distortion of behavioral (Bartholomew et al., 2015) and physiological measurements (Colinet and Renault, 2012), which, depending on the form of anaesthesia, can last for hours or days (MacMillan et al., 2017). Even seemingly benign manual aspiration can disrupt the expression of sensitive phenotypes (Trannoy et al., 2015). Automated, anesthesia-free animal-handling thus has the potential to standardize handling effects.

MAPLE’s modularity means that the platform's versatility and capabilities can be expanded in the future. As examples, we are developing fly sink modules for culturing thousands of animals in the style of population cages, facilitating selection experiments and experimental evolution. We are developing a high-resolution imaging source/sink module into which flies could be deposited, imaged in dorsal and ventral views, and released back into experimental workflows. This device is inspired by the Fly Catwalk system (Medici et al., 2015) but can be loaded with a fly on demand, rather than relying on flies to enter the imaging chamber on their own. The images from this module will allow machine-learning-based classification of morphological phenotypes, like sex and eye color, as well as quantification of fluorescent proteins; key genetic markers in flies.

In addition to conducting tedious or complex experiments, MAPLE’s integrated format affords the opportunity to feed back the results of phenotypic assays into fly handling tasks. For example, MAPLE could be used to perform artificial selection, identifying individuals with a specific behavior or phenotype, and placing males and females together in wells of a FlyPlate, vials, or population cage. Likewise, multigeneration genetic crosses will be possible with our high resolution imaging module. Our current behavioral phenotyping relies on simple motion tracking, but behavioral assays employing thermogenetic or optogenetic stimulation, or sophisticated stimulus control could be implemented through custom modules. Unsupervised learning algorithms have been used to automatically phenotype flies (Berman et al., 2014; Todd et al., 2017), and with the capacity to store and access large numbers of flies individually, MAPLE could identify and isolate outliers within a population. MAPLE is compatible with such modern, automated approaches to fly experimentation, and brings automated animal-handling one step closer to the potential achieved by liquid-handling robots for molecular research.

CAD files for MAPLE can be found at https://github.com/FlySorterLLC/MAPLEHardware (copy archived at https://github.com/elifesciences-publications/MAPLEHardware). Control software for MAPLE including scripts for the experiments described here can be found at https://github.com/FlySorterLLC/MAPLEControlSoftware (copy archived at https://github.com/elifesciences-publications/MAPLEControlSoftware). Raw data and analysis scripts can be found at http://dx.doi.org/10.5281/zenodo.1119131. These materials are also available at http://lab.debivort.org/MAPLE.

CAD files for MAPLE can be found at https://github.com/FlySorterLLC/MAPLEHardware (copy archived at https://github.com/elifesciences-publications/MAPLEHardware). Control software for MAPLE including scripts for the experiments described here can be found at https://github.com/FlySorterLLC/MAPLEControlSoftware (copy archived at https://github.com/elifesciences-publications/MAPLEControlSoftware). Raw data and analysis scripts can be found at http://dx.doi.org/10.5281/zenodo.1119131. These materials are also available at http://lab.debivort.org/MAPLE.

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Author details

1. Tom Alisch

   1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
   2. Center for Brain Science, Harvard University, Cambridge, United States

   Contribution

   Resources, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7884-7031

2. James D Crall

   1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
   2. Planetary Health Alliance, Harvard University, Cambridge, United States

   Contribution

   Resources, Software, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-8981-3782

3. Albert B Kao

   Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States

   Contribution

   Resources, Software, Investigation, Methodology, Writing—original draft, Writing—review and editing

   Competing interests

   No competing interests declared

4. Dave Zucker

   FlySorter LLC, Seattle, United States

   Contribution

   Conceptualization, Resources, Software, Supervision, Funding acquisition, Validation, Investigation, Methodology, Writing—original draft, Project administration, Writing—review and editing

   Competing interests

   Dave Zucker is the founder and manager of FlySorter, LLC, which was originally included as his primary and sole affiliation.

   "This ORCID iD identifies the author of this article:" 0000-0003-0259-6293

5. Benjamin L de Bivort

   1. Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, United States
   2. Center for Brain Science, Harvard University, Cambridge, United States
   3. FlySorter LLC, Seattle, United States

   Contribution

   Conceptualization, Software, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Writing—original draft, Project administration, Writing—review and editing

   For correspondence

   debivort@oeb.harvard.edu

   Competing interests

   Benjamin de Bivort is on the scientific advisory board of FlySorter, LLC.

   "This ORCID iD identifies the author of this article:" 0000-0001-6165-7696

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