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Fly strains were reared on standard Drosophila medium at 25°C in complete darkness. Or42b-Gal4 (Bloomington Stock No: 9972), Or47a-Gal4 (Bloomington Stock No: 9982), UAS-ChR2-XXL (Bloomington Stock No: 58374) and w¹¹¹⁸ (obtained from R. Stocker) were used. Strains crossed with w¹¹¹⁸ served as controls. For all the behavioral experiments, the flies were transferred to new vials and allowed to lay eggs for 2 days. Third instar feeding-stage larvae aged 96–144 h were used for behavioral experiments.

Petri dishes (85 mm diameter; Cat. No. 82.1472, Sarstedt, Nümbrecht) were used as the test plates, as described previously (Pauls et al., 2010b; Huser et al., 2012, 2017; Gerber et al., 2013). The test plates and training chambers were filled with 2.5% agarose (Sigma-Aldrich, Cat. No. A9539, CAS No. 9012-36-6). In several behavioral experiments 0.01 M lithium chloride (Sigma-Aldrich, Cat. No. 298328, CAS No. 85144-11-2) was mixed with 2.5% agarose. Throughout the test, the Petri dishes were covered with perforated lids for an equal distribution of odors. All the experiments were performed at about 21°C. As olfactory stimuli in the test we used 10 μl amyl acetate (AM, Sigma-Aldrich, Cat. No. 46022; CAS No. 628-63-7; diluted at 1:100, 1:250, 1:500, 1:600 and 1:750 in paraffin oil, Sigma-Aldrich, Cat. No. 76235, CAS No. 8012-95-1), benzaldehyde (BA, undiluted; Sigma-Aldrich, Cat. No. 12010, CAS No. 100-52-7) and ethyl acetate (EA, Sigma-Aldrich, Cat. No. 270989; CAS No. 141-78-6; diluted 1:1000 in paraffin oil, Sigma-Aldrich, Cat. No. 76235, CAS No. 8012-95-1). Odorants were loaded into custom-made Teflon containers (4.5-mm diameter) with perforated lids (Scherer et al., 2003) and were used for no longer than 5 h after preparation.

The Maggot Instructor consists of a training box wired with a computer that controls the type and timing of the applied stimuli via a cRIO system and an automated training device (ATD) (Graetzel et al., 2010; Kain et al., 2012; Dylla et al., 2017). cRIO (NI 9074) from National Instruments was used as a controlling device for the automated training protocol. cRIO was also used to regulate and monitor the technical aspects, such as the fine adjustment of parameters (e.g., light intensity, voltage, or temperature). The software Build Digital Output Sequence with Frequency Output (BDOS) was used for programming cRIO (Dylla et al., 2017). All settings in cRIO were transmitted to the training box (see below), where the parameters, including electric shock or light intensity were adjusted appropriately. Larval training was carried out in an elongated metal box (the training box), which was separated into 10 training chambers with the same size and can be regulated in parallel or individually. Each chamber consists of a case with an electrode at the front and rear end, a Peltier element underneath the chamber and odor inlets and outlets on all four sides. The training chamber is closed by a lid, which contains a white and a blue LED.

Only L3 larvae that are in the feeding stage were used. This requirement was achieved by collecting the larvae from the top layer only of the food substrate. Ten groups with 30 larvae each were collected, washed with tap water, and stored in a water drop for up to 30 min before the experiment. To avoid artificial activation of ORNs in the experimental animals, these steps were performed under red light. Before the experiment, the training chambers were filled with 2.5% agarose to cover the entire bottom with a substrate layer of about 1 cm thickness. After the preparation, the larvae were transferred to the training chambers. The larvae from every genotype were used in each run. For several runs, the training chambers were consistently varied for each genotype. Several runs were possible per training chamber with the same agarose substrate. To prevent the larvae from escaping the training chambers, a custom-made plastic frame covered with a plastic net was inserted into each training chamber. This technique was established by Khurana et al. (2009). This method also prevented the larvae from climbing the training chamber and thus avoiding electric shock. The training chambers were also moistened with about 1 ml of tap water to ensure the proper hydration of the larvae. Afterward, the lids of each training chamber and the cover of the Maggot Instructor were closed. The device was switched on, and the previously defined training protocol was started. All the training steps including CS (if not otherwise mentioned at a light intensity of about 86,000 lux) and US (if not otherwise mentioned electric shock of 120 V) application, then ran automatically. The training lasted for 60 min.

After training, the cover of the Maggot Instructor and the lids of each training chamber were removed. For the test, the larvae from each training chamber were placed on a fresh, pure agarose assay plate with an odor container on the one side and a second container without olfactory cue on the other side. The sides were randomly changed for every training chamber. All the larvae from one training chamber located on the plastic frames and the agarose cover bottom were collected and transferred. The larvae were placed in the center of the Petri dish, the lid was closed, and the larvae were given 5 min to freely move on the test plate. Ten test plates were analyzed in parallel (one for each training chamber). A Preference Index was calculated by subtracting the number of larvae on the control container side (CC) from the number of larvae on the odor side (ODOR) and dividing the result by the total number of larvae on both sides and in the middle zone (TOTAL):

The positive values indicate attraction to the odor, whereas the negative values represent aversion.

All data processing, statistical analyses, and visualizations were conducted with GraphPad Prism 7.0a. Figure alignments were performed with Adobe Photoshop CC. The groups that showed no violation of the assumption of normal distribution (Shapiro–Wilk test) and homogeneity of variance (Bartlett’s test) were analyzed with parametric statistics. One-way ANOVA was applied followed by planned pairwise comparisons between the relevant groups with a Tukey’s honestly significant difference post hoc test (comparisons between groups larger than two). Experiments with data that significantly differed from the assumptions above were analyzed with the non-parametric Kruskal–Wallis test followed by Dunn’s multiple pairwise comparison. To compare single genotypes against chance level, we used one sample t-test or Wilcoxon signed-rank test. The significance level of statistical tests was set to 0.05. Data were presented as box plots, with 50% of the values of a given genotype being located within the boxes and the whiskers representing the entire set of data. Outsiders are indicated as dots. The median performance index was indicated as a bold line and the mean as a cross within the box plot.

A comprehensive set of standardized behavioral assays is available to analyze learning and memory in Drosophila larvae (Gerber and Stocker, 2007; Widmann et al., 2017). These approaches all require the larvae to be transferred manually several times from one Petri dish to another during the procedure and are thus labor intensive. To overcome this limitation, we aimed to develop a new, robust, and easy-to-handle device, which we named Maggot Instructor, to train Drosophila larvae in an automated fashion. The device consists of a training box connected to a computer that controls the type and timing of the applied stimuli via a cRIO system and an ATD (Figures 1A,D) (Dylla et al., 2017). Both are programmed by simple and flexible customizable training protocols using a BDOS software (Dylla et al., 2017). The training box consists of 10 separate training chambers that can be regulated in parallel or individually (Figures 1A,B). Therefore, one can train up to 10 groups of larvae in this device in parallel to increase the throughput. Each training chamber consists of a case, in which an electrode is incorporated at the front and the rear end (Figure 1C, above). In addition, a Peltier element is placed underneath the chamber and the odor inlets and outlets on all four sides (Figure 1C, above). The training chamber is closed at the top by a lid equipped with a white and a blue LED (Figure 1C, below). Therefore, the larvae can be exposed to the following stimuli: cold, heat, air, electric shock, and light (white and blue). Additional technical details are included in Figure 1, in Section “Materials and Methods,” or are available upon request. Our initial study focused on a protocol that automatically conditions the larvae by optogenetic activation of ORNs (CS) via blue light and stimulation through electric shock (US).

As shown in several studies, Drosophila larvae can establish an aversive olfactory memory by associating an odor with an electric shock (Aceves-Pina and Quinn, 1979; Heisenberg et al., 1985; Tully et al., 1994; Khurana et al., 2009; Pauls et al., 2010a). The current model suggests that the olfactory information is signaled from ORNs via PNs to MB KCs (Ramaekers et al., 2005). MB KCs, which are third-order olfactory neurons, are also stimulated via DANs, which signal a negative reinforcement (Selcho et al., 2009). When both stimuli coincide, synaptic plasticity occurs. These changes imply that in the following test, MBONs can be addressed by the learned odor to trigger the learned behavior (Figures 2A,B). In the standard assays, odors are used as CS. However, extensive preliminary tests have shown that using odors lead to different problems, including sticking to the agarose substrate in the training chamber (data not shown). Agarose is required to provide a substrate on which larvae can crawl easily and to prevent the larvae from drying out (Apostolopoulou et al., 2014). For this reason, we decided to train the larvae not with real odors but through the optogenetic activation of individual ORNs. Honda et al. (2014) have shown that the artificial optogenetic activation of a single ORN is sufficient to induce an associative olfactory memory in Drosophila larvae.

The two-odor reciprocal training paradigm is a widely used method to study associative olfactory learning and memory in larvae (Aceves-Pina and Quinn, 1979; Gerber and Stocker, 2007; Schipanski et al., 2008; Eschbach et al., 2011; von Essen et al., 2011; El-Keredy et al., 2012; Widmann et al., 2017). The use of a similar design would therefore allow for the comparison of larval odor-taste and odor-electric shock learning and memory in general. However, in an early study, we have shown that this design features several caveats (Pauls et al., 2010a). (i) The method yields relative low performance scores and thus may cause difficulty in the comparative studies of genetically manipulated larvae. (ii) This drawback may be partially overcome by increasing the number of training cycles but trigger starvation-dependent effects. (iii) The two-odor design causes a sequence effect as differences are observed in the performance depending on whether the first (CS1) or second odor (CS2) has been punished. To overcome these concerns, we decided to use exactly the same one-odor non-reciprocal training design parameters, which we have established in our previous work (Pauls et al., 2010a).

The automated training protocol consists of a 60 s blue light phase, in which an electric shock is applied during the last 30 s, followed by a 300 s resting phase in complete darkness (Figures 2C,E). The training trial is repeated 10 times (from now on called 10-cycle training). Immediately thereafter, the larvae are tested for 5 min for their odor preference for a specific odor over paraffin oil, which serves as the control (Figure 2D). The test therefore requires a manual step.

To demonstrate that Drosophila larvae can be trained in an automated fashion via the Maggot Instructor, different parameters had to be tested in advance. We used the artificial blue-light dependent activation of Or42b-Gal4 and Or47a-Gal4 crossed with UAS-ChR2-XXL to specifically activate ORN 42b and 47a, respectively (Dawydow et al., 2014; Honda et al., 2014). Both lines were reported to be single-cell-specific (Fishilevich et al., 2005). ORN 47a was reported to specifically encode the odor amyl acetate (AM), whereas ORN 42b encodes the odor ethyl acetate (EA) (Kreher et al., 2005; Hoare et al., 2011).

We initially focused our analysis on ORN 47a and checked whether the larvae that express ChR2-XXL in ORN 47a can perceive odors. The larvae were tested for their naïve olfactory choice behavior between an odor-filled container on one side and a container without olfactory cue on the other side of a Petri dish (Figure 3). This test was performed with either AM or benzaldehyde (BA) as odor stimuli (Figures 3A,B). Or47a-Gal4/UAS-ChR2-XXL larvae are attracted by the odor AM (Figure 3C). This behavioral response shows no significant difference from both the control groups (Or47-Gal4/+ and UAS-ChR2-XXL/+) (Figure 3C). Similarly, BA is attractive to Or47a-Gal4/UAS-ChR2-XXL larvae, and the response is comparable in both control groups (Or47-Gal4/+ and UAS-ChR2-XXL/+) (Figure 3D). We concluded that the expression of ChR2-XXL in ORN 47a exerts no influence on the naïve odor perception of the larvae.

We then tested whether the activation of ORN 47a, together with an electric shock leads to a reduction in the odor preference for AM (Figure 4). This reduction would indicate that an aversive olfactory memory was formed. We performed five different experiments in which the light intensity and the voltage of the electric shock remained unchanged during training, but the dilution of AM in paraffin oil in the test was either 1:100, 1:250, 1:500, 1:600, or 1:750 (Figures 4B–F). During training via the Maggot Instructor, all larvae received the 10-cycle training as described before (Figures 2E, 4A). As a result, we observed that for the dilutions 1:100, 1:250, and 1:500, the Or47a-Gal4/UAS-ChR2-XXL larvae showed a reduced olfactory preference for AM compared with both genetic control groups (Or47a-Gal4/+ and UAS-ChR2-XXL/+) (Figures 4B–D). No difference was observed between the three groups when the dilution of AM was 1:600 or 1:750 in the test (Figures 4E,F). These results suggest that associative olfactory conditioning using the Maggot Instructor is feasible, and Drosophila larvae are very likely able to establish an aversive odor-electric shock memory. However, the memory can only be revealed at high odor concentrations. The olfactory preference for AM for both the control groups (Or47a-Gal4/+ and UAS-ChR2-XXL/+) statistically significantly differed from each other when a dilution of 1:500 was used (Figure 4D). Nevertheless, we decided to continually use this odor dilution as the experimental group (Or47a-Gal4/UAS-ChR2-XXL) features a specific behavioral phenotype in comparison with both the control groups (Or47a-Gal4/+ and UAS-ChR2-XXL/+), and we have used the lowest possible odor concentration to avoid the harmful side effects.

Next, we performed a parametric analysis with varying voltage of the applied electric shock and intensity of the artificial blue light activation (Figures 5, 6). We used the established 1:500 AM dilution and the 10-cycle protocol (Figure 5A) and tested whether electric shocks applied at 60, 90, or 120 V cause different effects on learning and memory (Figures 5B–D). As a result, we noted that for electric shocks of 60 and 120 V, in contrast to 90 V, Or47a-Gal4/UAS-ChR2-XXL larvae showed a reduced olfactory preference for AM compared with both the genetic control groups (Or47a-Gal4/+ and UAS-ChR2-XXL/+) (Figures 5B–D). Based on this results, we continually used 120 V for electric shocks, as all larvae survived this treatment and showed slightly stronger differences between the experimental group and both controls.

Next, we used the 1:500 AM dilution, 10-cycle, and 120 V protocol (Figure 6A) to test whether three different blue light intensities (50%, 75%, or 100%) cause different effects on learning and memory (Figures 6B–D). We noted that for blue light intensities of 100%, Or47a-Gal4/UAS-ChR2-XXL experimental larvae showed a reduced olfactory preference for AM compared with both genetic controls (Or47a-Gal4/+ and UAS-ChR2-XXL/+) (Figure 6D). By contrast, when trained with blue light intensities of 50% and 75%, the Or47a-Gal4/UAS-ChR2-XXL larvae showed no significant reduction in their preference for AM compared with both or at least one genetic control (Figures 6B,C; for blue light intensities of 50%, a significant difference was observed between Or47a-Gal4/+ and Or47a-Gal4/ChR2-XXL). Based on this result, we used a blue light intensity of 100% for follow-up experiments.

Previous studies that used LiCl reported an increase in larval memory scores for odor-electric shock learning as it makes the agarose substrate electrically conductive while being tasteless for larvae (Aceves-Pina and Quinn, 1979). However, this effect could not be confirmed by a study from our laboratory (Pauls et al., 2010a). Nonetheless, we determined whether the use of LiCl affects the automated Maggot Instructor training as its intake might cause harmful effects for the larvae and was reported to modulate adult behavior (Ries et al., 2017). The obtained data revealed that the use of LiCl is not necessary in our setup (Figure 7B), similar to our published data (Pauls et al., 2010a).

Prolonged blue-light activation of the sensory neurons via ChR2-XXL can lead to a decrease in firing of the cells (Dawydow et al., 2014). Therefore, we tested whether pulsed blue light activation of ORN 47a may produce a stronger behavioral effect. Instead, of a constant blue light activation of 60 s we used an alternating 1 s on-off regime. In this case, Or47a-Gal4/UAS-ChR2-XXL experimental larvae showed a significant reduction in their odor preference compared with the Or47a-Gal4/+ and UAS-ChR2-XXL/+ control groups (Figure 7C). Direct comparison of the performance of Or47a-Gal4/UAS-ChR2-XXL larvae at pulsed light (Figure 7C) and constant light (Figure 7D) showed a significant difference in the odor preference between both groups. This result indicates that the optogenetic activation with pulsed light featured a weaker effect on reducing odor preferences for AM than with constant light. Therefore, we continually used the 1:500 AM dilution, 10-cycle, 120 V, and 100% constant blue light protocol on the agarose filled training chambers without LiCl.

The conditioning regime used by the Maggot Instructor lacks reciprocity. The regime defines learning and memory as a reduction in AM preference between an experimental group and two genetic control groups. We thus designed two additional control experiments to ensure that neither blue light activation nor electric shock stimulation alone specifically can change the AM preference of Or47a-Gal4/UAS-ChR2-XXL larvae (Figures 8A,B). Although unlikely, significant differences between the experimental and control groups would suggest that the obtained phenotype would be based on non-associative effects rather than associative learning and memory. As expected, both results showed no reduction in the AM preference of the Or47a-Gal4/UAS-ChR2-XXL larvae compared with the Or47a-Gal4/+ and UAS-ChR2-XXL/+ control groups (Figures 8C,D). These results show that the observed behavioral change in the experimental larvae after conditioning via the Maggot Instructor is based on associative learning and memory.

Next, we analyzed the odor specificity of the memory. Studies previously showed that artificial activation of a ORN during conditioning induces an odor-specific memory that overlaps with the response profile predicted for the respective ORN (Honda et al., 2014). Accordingly, we tested whether the artificial activation of ORN 47a can also establish odor-electric shock learning and memory for an odor that is not covered by the reported Or47a response profile. Considering Or47a, such case applies to BA (Kreher et al., 2005; Hoare et al., 2011; Munch and Galizia, 2016). As expected Or47a-Gal4/UAS-ChR2-XXL larvae showed an odor preference for BA, and this preference is indistinguishable from the both genetic control groups (Or47a-Gal4/+ and UAS-ChR2-XXL/+) (Figure 9B). Based on this result we conclude that odor-electric shock learning and memory established after training via the Maggot Instructor is specific for the activated ORN and thus overlaps with its reported response profile. We confirmed this result independently by reproducing the finding published for Or42b. Honda et al. (2014) reported that the artificial activation of ORN 42b paired with an artificial activation of octopaminergic neurons that encode for a rewarding function establishes an appetitive olfactory memory specific for EA. Using our standardized training protocol but the odor EA (1:1000) in the test (Figure 10A) Or42b-Gal4/UAS-ChR2-XXL larvae also established an aversive odor-electric shock memory (Figure 10B).

Drosophila larvae can establish different types of associative memory based on the pairing of two stimuli (US and CS) (Aceves-Pina and Quinn, 1979; Scherer et al., 2003; Gerber and Stocker, 2007; Widmann et al., 2017). In contrast to the almost exclusively manual assays that are currently in use, we showed that larvae can also be trained automatically with the help of the Maggot Instructor. Automation will allow one to conduct comprehensive behavioral screens of newly established genetic tools (Li et al., 2014; Saumweber et al., 2018). In several experiments, we have shown that genetically modified larvae, which still show a natural naïve odor preference (Figure 3), learn the temporal paired optogenetic activation of ORN 47a with an electric shock and store this experience as an aversive olfactory memory (Figures 4–7, 10). Our results showed that this memory is specific for the identity and concentration of odors as the odor-electric shock memory was only detectable at certain concentrations of AM (Figure 4) and not visible when BA was used in the test (Figure 9). The conclusion regarding the associative nature of the observed reduction in the AM preference is compelling as we also showed that other parameters per se, such as artificial activation and electric shock, caused no alteration in the tested olfactory behavior (Figure 8). Therefore, we conclude that training larvae via the Maggot Instructor leads to an odor-specific associative process. The formation of memory by artificial activation of ORNs is not limited to ORN 47a given that an EA memory can be formed through the activation of ORNS 42b (Figure 10). However, for each of the 21 ORNs, odor-specific associative processes have to be tested, as several studies have shown the presence of non-equivalency among larval ORNs (Mathew et al., 2013; Hernandez-Nunez et al., 2015; Newquist et al., 2016). ORN 42a, for instance, unlike many other larval ORNs was shown to respond to a wide range of odors (Kreher et al., 2005; Hoare et al., 2011; Mathew et al., 2013).

To establish an associative olfactory memory in Drosophila larvae, the animals with natural stimuli, such as an odor and an electric shock, must be conditioned (Aceves-Pina and Quinn, 1979; Pauls et al., 2010a). However, the precise control of natural stimuli often presents difficulty. Therefore, thermogenetic and optogenetic effectors, such as TRPA1 and ChR2, that are expressed via transgenic techniques provide an alternative as they allow for the precise control of the activity of defined neurons in living larvae (Hamada et al., 2008; Dawydow et al., 2014). Associative olfactory conditioning theoretically includes the CS (odor) and/or the US (reward/punishment) pathways. Schroll et al. (2006) showed that light-induced activation of a set of DA neurons paired with an odor stimulus induces aversive memory formation, whereas activation of OA neurons induces appetitive memory formation. These results could be extended by demonstrating that in downstream of the OA neurons, the activity of four DA pPAM is also sufficient to trigger an appetitive memory (Rohwedder et al., 2016). For two of these DA neurons, activating them individually is enough for memory formation (Saumweber et al., 2018). In summary, these studies showed that substation experiments can be possibly carried out for the US in the larva, both for appetitive and for aversive learning, up to the single-cell level. This condition also holds true for the adult Drosophila. By contrast, a successful CS substitution at the level of ORN has thus far only been shown for the larva stage (Honda et al., 2014). Perhaps, the reason is the simpler neural network or the organization of parts of the larval olfactory pathway as a labeled line up to the MB (Ramaekers et al., 2005; Berck et al., 2016; Eichler et al., 2017). The optogenetic activation of ORN 24a and ORN 42b paired with the thermogenetic activation of most OA neurons induces an appetitive memory for acetophenone and EA, respectively (Honda et al., 2014). In this study, we showed for the first time the establishment of an aversive memory via CS substitution (Figure 10). Taken together the activation of ORN 42b serves the classical CS function. The pairing of ORN 42b activation via a natural odor or artificially via blue light and a reward or punishment causes the CS to trigger attraction or avoidance. As a consequence, appetitive and aversive associative learning processes can now be generated artificially, temporally, and spatially in various combinations in the larval brain and independent of natural stimuli. In this situation, the Maggot Instructor can be helpful. Thus, in future experiments, the order of CS and US, their precise timing (e.g., backward and forward conditioning; delay conditioning), and additional parameters, such as the number of training cycles or the strength of the CS and the US, can be analyzed in a controlled manner. The same condition applies to the neuronal networks. Activation experiments for PNs, sets of KCs, MBONs, and screens for identifying neurons of the US pathway would be conceivable.

The associative olfactory learning and memory that we tested with ORN 47a was specific for AM (Figures 4–9). However, we opted not to analyze in-depth the odor specificity of the memory. The tuning curve for the receptor Or47a is very specific at low odor concentrations (10^-4) and responded almost exclusively to AM when tested for 26 different odors (Kreher et al., 2008). This result was also confirmed by a second study, which has tested for 19 different odors (Hoare et al., 2011). We used these results to select Or47a for our experiments. At a higher concentration (10^-2), the receptor specificity changes, and in addition to AM, one also sees responses to other odors, such as propyl acetate, isoamyl acetate, 1-octen-3-ol, and 2-heptanone. For the receptor Or42b, this condition is very similar. At low concentrations (10^-4) Or42b shows high specificity for EA. At high concentrations (10^-2) responses for ethyl butyrate, propyl acetate, 2,3-butanedione and potential AM are reported (Kreher et al., 2008; Hoare et al., 2011). The high throughput rate of the Maggot Instructor allows repetition of these physiological experiments at the behavioral level to identify the tuning curves for each ORN in relation to many odors after olfactory learning and memory. These experiments would provide more information on the neural principles of larval odor processing to better understand the odors that larvae can learn and remember.

The Maggot Instructor shortens the time necessary to perform an experiment. The manual training protocol consists of 60 s CS and US pairing followed by a 300 s resting phase in complete darkness (Figures 2C,E). This training trial is repeated 10 times and spans 60 min in total (Pauls et al., 2010a). Although the Maggot Instructor, compared with the manual protocol, requires about the same time to prepare the larvae before and test them after training, the training itself requires no handling. A standard experiment usually consists of an experimental group, a driver and reporter control, each with about 10 repetitions per genotype. This situation results in a time of approximately 3 (genotypes) × 10 (repetitions) × 60 min, or 30 h saved per complete experiment.

Although this rough estimate shows the immense time saved, one must also mention that large genetic screens cannot be achieved immediately. The Maggot Instructor requires ChR2 to be expressed in individual ORNs. This goal can be achieved either via direct Or promoter ChR2 fusion constructs, via the LexA, the Q, or the Gal4/UAS system (Brand and Perrimon, 1993; Lai and Lee, 2006; Potter et al., 2010). However, as these tools are either non-existent, rare, or problematic, and as they affect other genetic modifications, genetic screens require a special strategy to deploy the Maggot Instructor. For example establishing the Or47::ChR2-XXL larvae would be possible. This construct can either be combined with a MB-Gal4 line to screen for the requirement of individual genes using available UAS-RNAi lines or with UAS-shi^ts to use available Gal4 and split-Gal4 lines to identify the neuronal circuits and individual neurons required for learning and memory (Kitamoto, 2001; Pfeiffer et al., 2010; Li et al., 2014). Alternatively, one can combine Or-LexA with LexAop-ChR2-XXL to artificially activate individual ORNs (Selcho et al., 2017). However, to date, to our knowledge, only Or47b-LexA (Hueston et al., 2016), which is not expressed in the larval olfactory system, has been published; thus, one would have to establish in any case new genetic tools before one can use the Maggot Instructor for large genetic screens.

In this work, we exclusively focused on the aversive olfactory memory reinforced with electric shock. The design of the Maggot Instructor, however, allows a whole series of other applications. Drosophila larvae can also associate odor information with light or heat punishment (von Essen et al., 2011; Khurana et al., 2012). The Maggot Instructor can apply these stimuli automatically. Furthermore, the Maggot Instructor offers the possibility to analyze associative visual learning and memory by pairing a light stimulus with electric shock. Such a protocol is already established as a manual assay (von Essen et al., 2011).

Extensive double activation experiments are also now possible. Defined ORN activation (standardized CS) can then be paired with activation of individual sensory neurons expressing gustatory receptors, ionotropic receptors, transient receptor potential cation channels, and/or pickpocket ion channel genes (Clyne, 2000; Dunipace et al., 2001; Scott et al., 2001; Liu et al., 2003; Montell, 2005; Benton et al., 2009). In this manner, one could comprehensively identify the sensory neurons that encode for appetitive and aversive reinforcement in Drosophila larvae (e.g., Gr93a for aversive reinforcement and IR60c potentially for appetitive reinforcement) (Apostolopoulou et al., 2016; Croset et al., 2016).

In summary, the range of applications of the Maggot Instructors extends well beyond the one shown here. Therefore, we confidently present in this work a very useful device that allows more rapid analysis of the behavioral, neuronal, and molecular fundamentals and different forms of larval learning and memory in the future.