To enable automated, high-throughput detection of pumping in animals crawling on culture plates, we combined epi-fluorescence microscopy with a large area scan camera (Figure 1A). Typically, pumping is detected by manual or automated counting using high magnification to resolve the motion of the grinder in the terminal bulb (Dallière et al., 2016; Bhatla et al., 2015). Using animals expressing a fluorescent protein in the pharyngeal muscle allowed us to image at a lower magnification compared to the resolution required for bright-field imaging of the grinder. Specifically, we image animals expressing YFP under the pharyngeal myosin promoter myo-2p (gnaIs1 [myo-2p::yfp]), which is present in all pharyngeal muscles except pm1 and pm2 (Okkema et al., 1993; Okkema and Fire, 1994).

By using a low magnification of 1x, we could image a field of view of 7 by 5 mm, corresponding to multiple body lengths of the worms (Figure 1A). We simultaneously imaged tens of animals (typically 30–50) as they crawled and analyzed their behavior off-line using our custom analysis software (Figure 1B; Video 1). The analysis pipeline combines a particle-tracking workflow with custom shape segmentation of the fluorescent pharynx (Figure 1C). After detecting and tracking the pharynges in the field of view, the contour and centerlines are fitted. The centerline and width are used to virtually straighten the animal (Figure 1D). We then extract a pump metric from the straightened images based on the standard deviation of the fluorescence along the dorso-ventral axis (DV-axis) of the animal, which reflects pumping events (Figure 1E and F). By averaging images during these putative detected pumping events, we determined that this metric is sensitive to the opening of the pharyngeal lumen and contraction of the terminal bulb and thus indeed corresponds to pumping events (Figure 1G).

Although the low magnification (1x) we use to image the animals allows us to increase the number of observed animals, this could compromise pumping detection. To determine how accurate our software detects pumping in these imaging conditions, we compared the results of our automated method and a manual annotator. Since manual annotation of pumping rates is still widely used, but practiced at higher magnifications, we simultaneously imaged worms at a magnification of 10x (pixel size 240 nm/px) in bright field and fluorescence (Figure 1H; Video 2). A human expert counted pumps in the videos acquired using the bright-field channel. We then ran our automated analysis on the video acquired on the fluorescence channel, but downscaled to 1x (pixel size 2.4 µm/px). We found that PharaGlow was able to accurately detect pumping in these videos, and the resulting rate and counts were in agreement with the human expert. Both methods result in a comparable mean pumping rate for the animals counted (Figure 1I), with a deviation between the human and automated results of less than 2 pumps per 10 s (Figure 1J). To score a typical experiment of 30 animals over 5 min of recording time, the human experimenter would need, at best, to count for at least 150 min of data (real time). This time is regularly longer, as accurate counting often requires scrutinizing the recordings in slow motion or visualizing the same part of the recording several times. PharaGlow is therefore able to automatically and reliably detect pumping in low-resolution, large field of view images, enhancing the number of animals which can be scored simultaneously.

Having developed this new high-throughput method which enables accurate measurements of many animals simultaneously, we wondered how pumping changes over the course of development, where the animal changes its size and its energy needs. During development, the pharynx grows with the body (Knight et al., 2002), but the ratio between pharynx and body length decreases from L1 stage to adulthood (Avery and Shtonda, 2003). To investigate how pumping rates change during development, we imaged cohorts of synchronized worms consecutively over three days in the middle of each of the four larval stages and as young adults (YA). Animals were imaged directly on their culturing plates while moving freely in the field of view (Figure 2A). We accounted for the growing pharynx by adapting the magnification of our imaging system to achieve approximately the same spatial sampling of the pharynx at each stage (Figure 2B). Under these conditions, we were able to sample at least 150 trajectories per developmental stage. Altogether, more than 1000 animal tracks remained after filtering animals that spend less than one minute in the field of view. Filtering leads to over-proportionally reducing young adult trajectories since these animals traverse the field of view quickly despite the spatially proportional scaling. Nevertheless, we obtain large samples of animals due to new animals continually entering, with a total measured time of four animal-hours for the adult stage, and more than 10 animal-hours for the L1 stage. The average track duration is well over one minute with 1.9 ± 0.9 min (mean ±s.d.) for L1 and 1.6 ± 0.6 min for adults (Figure 2—figure supplement 1). These data represent up to two orders of magnitude more single worm pumping data than is obtainable with conventional methods (see Table 1).

We find that on-food pumping rates increase slightly over the course of the larval stages, but much less dramatically than the velocity increases over development, despite the substantial growth of both the body and the pharyngeal muscles (Figure 2C–F). Owing to time resolution and the large number of individual worms that can be analyzed using PharaGlow, it is possible to generate smooth probability density functions of pumping across the different larval stages (Figure 2G). A small fraction of animals did not show pumping during our recording (Figure 2H, 5 animal tracks in L1 with <0.5 Hz, <1% for all other conditions). We wondered if we had captured animals during lethargus, the period of sleep preceding each molt despite choosing the imaging time points in the middle of each larval stage and working with an age synchronized population. However, lethargus is incompatible with the observed velocities of these animals. Alternatively, it is possible that these animals transiently show satiety quiescence, which might be absent under these conditions in the larger YA population (You et al., 2008; Gallagher et al., 2013; Davis et al., 2018).

As we image unrestrained animals, we can simultaneously assess pumping and locomotor behaviors. Animals move forward on agar by generating waves of muscular contraction through their body. When the animals reverse the direction of these waves, they move backwards. Such spontaneous reversals are rare events, but can be triggered by diverse stimuli, such as nose touch (Chalfie et al., 1985) or heat (Zhao et al., 2003). The reversal rate depends also on the food condition and the developmental stage of the animal. In the absence of food, the reversal rate is higher in young adults than in larvae (about 45 events vs 30 events in 10 min), but constant throughout larval development (Chiba and Rankin, 1990). In our on-food measurements, we find some significant differences in reversal rates, however, the effect size is small (e.g. corresponding to a rate of 47 vs 49 pumps/10 min between L2 and L3 animals). The only strong difference appears between the earlier larvae L1-L3 and the later L4/young adult stages with a difference of approximately 10 reversals /10 min (Figure 2—figure supplement 2).

We investigated whether extended exposure to light might affect worm behaviors by monitoring the amount of reversals, pumping rate, and speed. Our results suggest that there is a mild light avoidance reaction (5–25%) which depends on the developmental stage (Figure 2—figure supplement 3). Repeated exposure did not affect behavior in most developmental stages, except for the young adult stage, for which a higher velocity was observed when exposed to light multiple times (Figure 2—figure supplement 4). Additionally, we tested whether light exposure caused phototoxic effects. Long-term exposure (up to 5 hr) did not affect worm viability. Lastly, different excitation light did not affect the pumping rate, but had a mild impact on velocity (Figure 2—figure supplement 5; see Appendix for details).

Overall, we find that our imaging approach can be adapted to larvae by increasing the magnification, and our analysis pipeline is capable of handling data from hundreds of animals. While there are small deviations between the automated detection and human counted data (Figure 2—figure supplement 6), we accurately detect both mean and individual rates for all stages, with a median of error between experts and our method of less than 10%. Over the course of three days and five imaging sessions, more than 1000 animals were tracked, significantly more than can be achieved with comparable methods (Table 1).

Next, we wanted to determine if our method was robust to changes in locomotion and plate context, allowing a wider range of applications such as investigating starvation or different pharmacological treatments without the presence of a bacterial food source. Off-food locomotion is faster (Dillon et al., 2016; Gray et al., 2005), and pumping irregular (Lee et al., 2017; Scholz et al., 2016), which could potentially be more challenging for detecting pumping. Prior work showed that pumping rates off food are lower, but increase over the course of starvation and that this increase is mediated by a cholinergic pathway (You et al., 2006). We track animals either on-, or off food over an increasing amount of starvation time and extract behavioral dynamics (Figure 3A–D). We confirm that pumping is dependent on the starvation duration, with a reduction in pumping rate over the course of three hours (Figure 3B, D). Beyond the first time point, our data are consistent with prior data (You et al., 2006), showing a sustained rate of around 2–2.5 Hz (Figure 3E). Previously, rates measured immediately after transferring worms off food (<30 min of starvation) were very low, possibly due to a lasting pumping suppression after harsh touch (Keane and Avery, 2003), which we avoid by washing worms off plates instead of picking (see Methods).

As we are able to measure pumping and locomotion behaviors simultaneously, we wanted to see if we could observe co-regulation of locomotion and feeding off-food. When taken off of food, C. elegans displays a restricted area search (local area search) which is characterized by frequent turns and reversals and an elevated speed (Gray et al., 2005; Hills et al., 2004; Sawin et al., 2000; Calhoun et al., 2014). This behavior lasts between 30 and 60 min, after which animals switch to longer runs that cover more area, which is a strategy for dispersal (Hills et al., 2004; Wakabayashi et al., 2004). Interestingly, for starved worms at 30 and 90 min, the joint distribution of pumping rates and velocities show distinct sub-populations (Figure 3F). For longer starvation durations, the population becomes homogeneous with a well-defined mean pumping rate and speed. For the shortest starvation time point we sampled, we see a mixed population with distinct speeds and pumping rates, possibly reflecting some animals that are still performing a local search and others that are not. This is consistent with the fact that these distinct populations are not apparent in worm populations that stay on food (Figure 3—figure supplement 1).

To further investigate the origin of the two sub-populations observed at short starvation time, we analyzed the pumping rate distributions of individual animals (Figure 3G). Taken together, the data suggests that at 30 min starvation, a fraction of the animals show low speeds and pumping rates, and the remainder are in a high-speed, high pumping state (Figure 3F, G). This suggests two possible interpretations. First, it is possible that, with increasing starvation time, a subset of animals transitions to lower pumping rates until all animals show a similar pumping rate distribution with an average of ~2 Hz. Alternatively, the two sub-populations could result from transient behavioral changes among animals to high pumping rates. These transitions would occur less frequently with increasing starvation time. To discern among these two possibilities would require measuring single animals over longer periods of time. Further studies are required to reveal these population dynamics upon starvation.

Having established that PharaGlow can robustly detect locomotion and pumping behaviors across a range of conditions, we wanted to test if it is a suitable tool for long-term recordings. As a proof of principle, we imaged the interactions of a male and a hermaphrodite over the course of 74 min at 30 fps (Figure 4A and B).

As the resulting data volume would have been prohibitive, we implemented a live segmentation method that allowed us to only store the animals coordinates and the region of interest around each animal (Videos 3 and 4; Methods). We then calculated the distance between the animals, allowing us to identify mating events (Figure 4C and D). We find that the animals frequently interact over the course of 1 h with multiple close encounters (Figure 4D). The male also showed a long period of quiescence in both locomotion and pumping rate. Overall, the animals are closer at the beginning of the recording, but later spend time at larger distances (Figure 4E, left and right panels). Despite the long imaging duration, we still observe pumping at the end of the recording, indicating that we have sufficient signal remaining to detect pumping events. We confirm this observation by calculating photo-bleaching curves. We find that the decay time of the signal is 410±47 min (Figure 4—figure supplement 1), which indicates that it is possible to do continuous imaging over multiple hours. In this case, the recording was limited by the male escaping the enclosure, rather than loss of signal.

Having this multi-scale data allows observing both large-scale structure and smaller events in the data. We further examined the mating event displayed in Figure 4C. During the encounter, the male shows a larger velocity and performs many long reversals when the animals are close, as is typical for a mating attempt. It is also interesting to note that pumping does not completely cease during the attempt (Figure 4F). Despite being in the same arena, and covering most of the enclosure during the recording (Figure 4B), the velocity and pumping distributions differ strongly between the two animals (Figure 4G and H). While the distributions of the male are dominated by the long quiescence period, the hermaphrodite overall shows a bi-modal rate distribution with some infrequent pumping at 4 Hz. PharaGlow is therefore able to track behavior over more than an hour, and keep the identity of animals given that these are constrained to the field of view.

A desired capability for a high-throughput feeding tool is the ability to faithfully detect pumping rates in mutant animals which might have different pharyngeal contraction patterns and body motion, potentially increasing the difficulty of detecting pumping events. To determine if PharaGlow could faithfully detect pumping and locomotion in mutant animals, we wanted to assay a range of feeding and locomotion phenotypes. We therefore selected mutants with reported constitutively high (unc-31) or reduced (eat-18) pumping rates and different locomotory patterns (Raizen et al., 1995; McKay et al., 2004; Avery et al., 1993). UNC-31 is involved in dense-core vesicle release, and unc-31 mutant animals display reduced, uncoordinated locomotion on food (Figure 5A). We confirm that unc-31(e928) and unc-31(n1304) animals pump at rates comparable to wildtype. However, we see a bimodal distribution of rates with a fraction of animals showing markedly lower rates (Figure 5B). By looking at the individual animals' pumping rates, we find that unc-31 animals show long pauses in pumping, unlike wt animals (Figure 5F).

In contrast to unc-31, eat-18 mutant animals have no previously reported locomotor defects, but pump slower than wildtype (McKay et al., 2004). EAT-18 is expressed in the pharyngeal muscle and interacts with a nAChR subunit EAT-2 to form a functional acetylcholine receptor (Raizen et al., 1995; Choudhary et al., 2020). Feeding impaired mutants were previously reported to have reduced body lengths and widths (Mörck and Pilon, 2006). As expected, we found that eat-18 animals were smaller (Figure 5C) and developed more slowly (approximately 91 hr from egg to adulthood compared to 63 hr for wildtype). While we detected pumping events at an average rate of 1 Hz, the animals showed a different contraction pattern and timing than either unc-31 or wt animals (Figure 5D and E). We confirmed that eat-18(ad820) animals lack the ability to perform fast pumping bursts (Figure 5B and F) and the duration of a pharyngeal contraction is approximately doubled compared to wt (Figure 5E, Figure 5—figure supplement 1, Video 3). We do observe a higher pumping rate than previously reported for eat-18, where animals were reported to rarely pump during experiments (<0.5 Hz, Raizen et al., 1995; McKay et al., 2004). To verify that the detected motion is pumping and not peristaltic movements or other non-pharyngeal muscular motion, we verified the rate by inspecting individual videos. When verifying these sample animals, we did observe slow pumping bursts at the 1–2 Hz rates indicated by PharaGlow, suggesting that these animals are able to pump at this frequency (Figure 5E, arrows and Video 3). We also found that eat-18 animals showed significantly fewer reversals than wildtype, indicating a role for the nAch receptor in modulating reversals. This is likely mediated by extrapharyngeally located neurons, since eat-18 is reported to show expression not only in the pharyngeal muscle, but also in some unidentified somatic neurons (McKay et al., 2004).

Considering the split distribution of mean pumping rates we observed for unc-31 in our short term (5 min) recordings (Figure 5B), we wondered if these distributions reflect a persistent difference between animals or if instead the animals perform infrequent switches between high - and low pumping rate states. We therefore tracked animals for at least three hours on food, restrained to our field of view using a copper enclosure. By confining only a few animals (<5 in the field of view), we were able to maintain animal identity over the course of the experiment (see Methods) and quantify their pumping rate over at least 2 hr (Figure 5G). If the population reflects a snapshot of the overall dynamics, a large population measured for a short period of time should result in similar pumping rates as a few animals measured over long time periods. To test this hypothesis, we quantified the autocorrelation of the pumping rate for single worms revealing that pumping rates were correlated for different time scales for wt and unc-31 mutants, with the unc-31 mutants showing a more persistent pumping behavior (τ=9.6 min for unc-31(e928) and τ=11.0 min for unc-31(n1304)) compared to wt (τ=7.9 min) (Figure 5—figure supplement 2). These data indicate that on-food pumping rates are autocorrelated over multi-minute time-scales.

To further investigate the different persistence of pumping across the different animals, we quantified the transitions between states of pumping and no or low pumping rates. We define a ‘fast’ state as a period at which the animals pump at >2.5 Hz and ‘off’ states as the converse (see also a similar analysis in Lee et al., 2017). Wild-type animals displayed frequent switching between low and high pumping rates (Figure 5H and J). In contrast, unc-31 animals displayed infrequent switches, consistent with prior reports of constitutive pumping (Avery et al., 1993) and the role of neuropeptides such as PDF in regulating switches between foraging states (Flavell et al., 2013). Taken together, these results show that studying the underlying behaviors and dynamics in a worm population requires large statistics and long recordings. Depending on the desired data, both long-term recordings and short-term high-throughput measurements are accessible with PharaGlow.

Using a combination of low-magnification fluorescence imaging and dedicated analysis software, we show that it is possible to perform high-throughput, automated pumping detection of worms crawling on standard culture plates. There are some limitations to the method that are due to the reliance on a fluorescent indicator in the pharyngeal muscle and the handling of the large datasets that are generated. We find that uneven plates or improper focus leads to low signal-to-noise ratios. With careful focusing, imaging the center of evenly poured plates and using our custom peak detection method that is adaptive to the image quality (see Methods), these pitfalls can be mitigated. Additionally, once at focus, small variations of the worm height do not affect the result, as low-magnification imaging results in a large depth of field.

As it is necessary for our approach to label the pharynx, mutant characterization with our tool would require crossing all possible mutants with a fluorescent reporter strain, and albeit labor intensive this is nonetheless still a standard genetic practice when a reporter is used. While most experiments in this paper were performed with a homozygous, integrated reporter background (gnaIs1), we have also used extrachromosomal arrays with success (Figure 2—figure supplement 5), which allows the use of animals that have a myo-2 reporter as a co-injection marker, for example. In addition, since our tool relies on the detection of fluorescent protein, siblings losing the transgene on the plate will not interfere with the analysis.

To maximize the field of view, we have chosen the smallest spatial resolution at which we could reliably detect pumping in wildtype adults. To ensure detection in smaller animals, increasing the magnification in these cases is recommended, as we did to detect pumping in larvae. The final requirement is related to data management and handling. While the hardware requirements are restricted to equipment commonly available in many laboratories (a fluorescence dissecting microscope or a epifluorescence microscope and a Megapixel camera are needed), the data rate of the large area scan cameras is >6 GB/min. On a 4-core laptop, the expected analysis time is approximately 8 hr for 150 worm-minutes of data. While the analysis can be run on a laptop or desktop computer, runtime is much improved when using a computing cluster (see Supplementary materials).

C. elegans were grown on NGM plates at 20 °C. Worms were synchronized by letting adult gravid animals lay eggs for 2–3 hr, then removing the adults. The average time from egg to young adult stage for strain GRU101 (gnaIs1[myo-2p::yfp]) was 63 hr. Before the experiment, synchronized adults were washed off the culture plates with 1 ml of M9 and collected in an Eppendorf tube. Worms were allowed to settle for 1 min, the supernatant was removed and the tube was refilled with M9. Washing was repeated two more times. The washing was sufficient in that we did not observe animals remaining in the spots containing the remainder of M9 on the assay plates, suggesting that the bacterial amount was too diluted to induce dwelling behavior.

Imaging of worms at ×1 magnification was performed using a commercial upright epi-fluorescence microscope (Axio Zoom V16; Zeiss) equipped with a 1 x objective (PlanNeoFluar Z 1.0 x/N.A. 0.25). For imaging of YFP fluorescence, light from an LED lamp (X-Cite XYLIS) was reflected towards the sample using a dichroic mirror (FT 515; Zeiss) and filtered (BP 500/25; Zeiss). Emitted light was filtered using a band-pass filter (BP 535/30; Zeiss) and focused onto the camera sensor (acA3088-57um; BASLER) using a camera adapter with an additional 0.5 x magnification (60 N-C ⅔’’ 0.5 x; Zeiss). The power density of fluorescence excitation at the focal plane (0.24 mW/mm² at 500 nm) was measured using a power meter sensor (PS19Q; Coherent) with the corresponding controller (PowerMax; Coherent). Animals were imaged at 30 fps for 5 min unless otherwise indicated. For imaging of mCherry, the filter cube was replaced with a commercial filter set (64 HE; Zeiss). The resulting power density using this cube was 0.76 mW/mm² at the focal plane.

For long-term imaging, stroboscopic illumination (5ms light pulses) were used to reduce bleaching. Excitation light was synchronized with the camera exposure using the GPIO camera line and the TTL input of the LED lamp. Frames were collected using a custom software (LabVIEW). To reduce the amount of stored data and allow continuous recording using a standard computer (Celsius W520; Fujitsu), images were segmented automatically and only areas containing worms, and their coordinates within the image, were stored. This procedure allowed a data reduction by approximately 1000 fold. For imaging, a copper frame (5.3 x 3.75 x 1 mm) was filled with 2% low melting point agarose (Sigma Aldrich) in M9 and 2–5 µl of a 10-fold concentrated overnight OP50 culture was seeded on top. The frame was deposited into a 10 cm NGM plate, and worms were transferred to the agarose arena. To preserve the moisture of the preparation and prevent shrinking of the gel, about ⅓ of the agar at the outer rim of the plate was removed using a scalpel and the space was filled with 6 ml of M9. Animals were recorded for at least 3 hr and all animals that were continuously tracked for at least 2 hr were included in the analyses in Figure 5.

Dual imaging was performed using an upright microscope (BX51WI; Olympus) and a 10 x objective (UplanSApo, NA 0.4; Olympus). For bright-field imaging, light emanating from a near-infrared (780 nm) LED (M780LP1 and driver LEDD1B; Thorlabs) was filtered using a (785/62 BrightLine HC; Semrock) and projected onto the sample via the bright-field illumination condenser. To excite fluorescence, the Teal line from an LED lamp (Spectra X light engine; Lumencor) was filtered (513/17 BrightLine HC; Semrock) and projected onto the sample using a 520 nm long-pass dichroic (FF520-Di02; Semrock). Transmitted and emitted light were filtered using a 532 long-pass filter (BLP01-532R; Semrock). To simultaneously record images in bright-field and fluorescence, a dual-camera device was used (DC²; Photometrics). Light was split into two channels using a 695 long-pass dichroic mirror (695DCXRUV; Photometrics) and images were projected into two cameras (acA3088-57um; BASLER). Fluorescent light was band-pass filtered (550/49 Brightline HC; Semrock) before reaching the camera sensor. The exposure time (6ms) of one camera served to synchronize the acquisition of the second camera and the Lumencor light engine. Individual worms were manually tracked using a 3-axis motorized stage (X-LSM150A; Zaber).

Worms were pre-synchronized by hypochlorite bleaching, allowed to hatch overnight in M9 and then cultured on NGM plates with OP50 at 20 °C. On day 3 after pre-synchronization, worms were synchronized again by letting 20 gravid animals lay eggs for 2 hr per assay plate, then removing the adults and letting embryos grow for specific durations to reach the appropriate larval or adult stage (19 hr for mid-L1, 31 hr for mid-L2, 39 hr for mid-L3, 50 hr for mid-L4, 65 hr for young adults). For the assay plates, 40 µl of E. coli OP50 culture was spotted onto an empty 6 cm NGM plate a few hours before the synchronization and left to dry. Synchronized worms were imaged directly on their assay plates as described in section ‘Imaging setup’. The magnification for each stage was chosen to achieve an approximate pharynx length of ~60 pixels (2 x (1.18 µm/px) for L1, 1.5 x (1.57 µm/px) for L2, 1.4 x (1.69 µm/px) for L3, 1.3 x (1.81 µm/px) for L4 and the standard 1 x (2.36 µm/px) for young adults). Three assay plates were imaged once per stage, and three additional plates were imaged at each stage to test for photo-sensitivity.

Washed animals were transferred to the center of an empty 6 cm NGM plate at room temperature and left to recover for 15 min before imaging. The same plate was imaged at defined time points for progressively more starved animals (at 30 min, 90 min, 150 min and 210 min after being taken off food). The field of view was chosen randomly on the plate but was required to contain at minimum 3 worms at the beginning of the recording. For control, washed animals were transferred close to a 40 µl of E. coli OP50 lawn, which was spotted onto an empty 6 cm NGM plate a few hours before the recordings and allowed to dry. Acclimation time and recording are similar for starved animals.

Movies for individual animals were created from a large field of view and expert annotators counted pumps by displaying the movie using the cell counter tool in Fiji (Schindelin et al., 2012). The annotators were blinded to the movie conditions and to the other experts’ results.

The data from this manuscript is available at https://osf.io/fy4ed/. The code repository for the PharaGlow package can be found at https://github.com/scholz-lab/PharaGlow (Scholz, 2022). Imaging software can be found at https://github.com/scholz-lab/Acquisition_PharaGlow, (Bonnard, 2022 copy archived at swh:1:rev:29ae8971f66fa0722f4914deb4b70b7d53f76f07).