Odor-guided navigation is common across the animal kingdom (Baker et al., 2018). Olfactory cues inform an animal of its location in a natural environment (Boie et al., 2018), and allow it to adjust its locomotion to navigate an odor landscape in a goal-directed manner (Bargmann and Horvitz, 1991; Berg and Brown, 1972; Aceves-Piña and Quinn, 1979). Odor-guided navigation is an ethologically relevant task that is important for the animal’s survival, and it has been a useful framework with which to study genes and circuits underlying sensory-motor transformations (Calhoun and Murthy, 2017; Clark et al., 2013). We focus in particular on the navigation of small model organisms in continuous, non-turbulent gradients established by the spread of odorants from their sources due to diffusion and drift. How animals interpret these gradients and use them to inform their actions remains an active and productive area of research especially in genetic model systems like Caenorhabditis elegans and Drosophila melanogaster (Bargmann and Horvitz, 1991; Aceves-Piña and Quinn, 1979; Levy and Bargmann, 2020; Mattingly et al., 2021; Gomez-Marin et al., 2011; Gepner et al., 2015; Tastekin et al., 2018).

A major challenge is to quantitatively relate the animal’s behavior to the precise olfactory cue that the animal experiences moment-by-moment. Therefore, it is critical to precisely control the odor environment and record the sensory cues experienced by these animals. The need to control and measure odorants still pose a formidable challenge. While many techniques exist to present odor stimuli (Gorur-Shandilya et al., 2019; Kadakia et al., 2022), measure odor concentration (Tariq et al., 2021; Celani et al., 2014), or develop virtual odor environments in a lab (Yamada et al., 2021; Radvansky and Dombeck, 2018), it remains challenging for precise control and continuous monitoring of an odor landscape.

All approaches to generate odor landscapes in a lab environment must contend with the odor’s diffusivity and interaction with other substrates. Early approaches to control odor concentration relied on passive diffusion to construct a quasi-stationary spatial odor gradient, for example by adding a droplet of odorant in a Petri dish in a ‘droplet assay’ (Monte et al., 1989; Pierce-Shimomura et al., 1999; Louis et al., 2008; Iino and Yoshida, 2009). Diffusion places severe limits on the space of possible landscapes that can be created and on the timescales over which they are stable, and the created odor profile is sensitive to adsorption of odor to surfaces, absorption into the substrate, temperature gradients, and air currents, all parameters that are difficult to measure, model, or control. Microfluidics allow water-soluble odors to be continuously delivered to a chamber in order to provide spatiotemporal control (Chronis et al., 2007; Albrecht and Bargmann, 2011; Lockery et al., 2008). Microfluidics devices, however, are limited in extent, require water-soluble odors, and must be tailor-designed to the specific attributes of the animal’s size and locomotion. A microfluidic post-array has been shown to support C. elegans locomotion, but no similar microfluidic device has been demonstrated to support olfactory navigation of Drosophila larvae, for example.

We previously reported a macroscopic gas-based active flow cell that uses parallel flow paths to construct temporally stable odor profiles (Gershow et al., 2012). That approach allows for finer spatiotemporal control of the odor gradient, is compatible with Drosophila larvae, and works with volatile airborne-based odor cues. This device used an array of solenoid valves to generate programmable odor profiles, but perhaps because of its complexity has not been widely adopted.

Most methods to create an odor landscape do not provide a means for knowing or specifying the spatiotemporal odor concentration. In other words, while an experimenter may know that some regions of an area have higher odor concentrations, they cannot quantify the animal’s behavior given a precise concentration of the odor. This limits the ability to quantitatively characterize sensorimotor processing. To address this shortcoming, various methods have been proposed to measure odor concentration across space. For example, gas samples at specific locations have been taken and measured offline (Yamazoe-Umemoto et al., 2018).

In one of the most comprehensive measurements to date, Louis et al., 2008 and Tadres et al., 2022, used infrared spectroscopy to measure the spatial profile of a droplet-based odor gradient. They used this approach to study bilateral olfactory sensing and olfactory encoding in Drosophila larvae, clearly demonstrating the value of measuring odor landscapes. That work measured odor concentration at a relatively low temporal resolution ($\sim 1$ sample/min), only when animals weren’t present, and relied on a relatively expensive Fourier transform infrared spectroscopy machine.

In all of these previous works, measurements were performed offline, not during animal behavior, and the odor concentration was assumed to be the same across repeats of the same experiment, or when animals were present. But even a nominally stable odor landscape is subject to subtle but significant disruptions over time from small changes in airflow, from temperature variation, and from the odor’s interaction with the substrate, which can include absorption, adhesion, and reemission (Gorur-Shandilya et al., 2019; Yamazoe-Umemoto et al., 2018; Tanimoto et al., 2017; Yamazoe-Umemoto et al., 2015). This is challenging to account for and control within a single behavior experiment, and is even more difficult to account for across multiple instances of such experiments. Additional variability may also arise across experiments as a result of the introduction of animals, changes to agar substrates, and alteration in humidity or other environmental conditions. To recover the odor concentration that an animal experienced, there is a need to monitor odor concentration and animals’ behaviors concurrently.

Our previously reported flow cell used a photo-ionization detector (PID) sensor moved across the lid before behavioral experiments to measure the odor concentration across space at a single point in time (Gershow et al., 2012). During experiments, the total concentration of odor in the chamber was monitored concurrently with measurements of behavior. While this provided some assurances that the overall odor concentration was relatively stable, it did not provide any spatial information concurrently with behavior measurements.

Metal-oxide sensors are small and scalable digital gas sensors that have recently been used to monitor odor concentration in studies of olfaction (Schmuker et al., 2016; Drix and Schmuker, 2021; Tariq et al., 2021) especially in the context of turbulent odor plumes. Arrays of metal-oxide sensors have been used to measure spatial odor landscapes by sampling across space (Burgués et al., 2020). Here, we leverage these approaches to present a new multi-sensor odor array with metal-oxide sensors combined with a flow chamber in order to measure the odor landscape that an animal crawls in with high spatial and temporal resolution. The array of sensors can be used in two ways: the full array can be used to measure the generated gradient throughout the extent of the chamber, or parts of the array can be used on the borders to monitor, during behavioral experiments, the odor profile along the boundaries of the chamber. By varying flow rates and the sites of odor introduction, we show a variety of odor landscapes can be generated and stabilized.

To demonstrate the utility of the apparatus, we applied this instrument to quantitatively characterize the sensorimotor transformation underlying navigational strategies used by C. elegans and D. melanogaster larva to climb up a butanone odor gradient. Butanone is a water-soluble odorant found naturally in food sources (Worthy et al., 2018) that is often used in odor-guided navigation studies and is of particular scientific interest for studies of olfactory learning (Bargmann et al., 1993; Levy and Bargmann, 2020; Cho et al., 2016; Torayama et al., 2007). We show that the agar gel used during behavioral experiments greatly disrupts an applied butanone gradient, and we demonstrate a pre-equilibration protocol allowing generation of stable gradients taking into consideration the effects of agar. Moreover, we monitor these gradients during ongoing behavior measurements via continuous measurements of the odor profile along the boundaries of the arena.

Using these stable and continuously measured butanone gradients, we measure odor-guided navigation in animals by tracking their posture and locomotion as they navigate the odor landscape. We record chemotaxis behavior and identify navigation strategies in response to the changing odor concentration they experience. In C. elegans, we observe navigation to the odorant butanone employs similar strategies to those reported for other sensory-guided navigation conditions, such as salt chemotaxis (Iino and Yoshida, 2009; Dahlberg and Izquierdo, 2020; Luo et al., 2014), namely a biased random walk strategy that adjusts teh frequency of turns, known as pirouettes (Pierce-Shimomura et al., 1999), and a gradual veering strategy, known as weathervaning (Iino and Yoshida, 2009; Izquierdo et al., 2015). We also measure navigation strategies of a mutant C. elegans and implicate the interneuron AIB mutant as being important for olfactory-guided weathervaning. In D. melanogaster larvae, we identify runs followed by directed turns (Gershow et al., 2012; Louis et al., 2008; Gomez-Marin et al., 2011). By using concurrent measurements of behavior and odor gradient, we characterize olfactory navigation in these small animals on agar with known butanone odor concentrations, which for C. elegans has not been reported before.

We developed new methods for both generating and measuring odor gradients which we describe here. The systems are modular, scalable, and flexible. The components, which can be used independently of each other, can be fabricated directly from provided files using online machining services.

Flow chamber for generating spatiotemporal patterns of airborne odors

We first sought to develop a method of creating odor gradients that satisfied the following criteria:

1. The spatial odor profile should be controllable. Varying control parameters (e.g. flow rates, tubing connections) should result in predictable changes to the resulting odor landscape.

2. The odor profile should be stable and verifiable. The same spatial profile should be maintained over the course of an experiment lasting up to an hour, and this should be verifiable via concurrent measurements during behavior experiments.

3. The apparatus should be straightforward to construct and to use, and flexible to adapt to various experimental configurations, including using with either C. elegans or Drosophila larva, and with agar arenas of various sizes.

We constructed a flow chamber to control odor airflow across an arena (Figure 1a). Odor and humidified air are sourced from two bubblers, one containing pure water and the other an aqueous solution of odorant and water. Flow rates are controlled by separate mass flow controllers (MFCs) upstream of the bubblers. Downstream of the bubblers, the odor and air streams are divided into parallel sections of equal lengthed tubing. Each tube is connected to one input port of the flow chamber. The pattern of connections and the flow rates set by the two MFCs determines the shape of the produced odor profile. For instance, if odor is provided at a single central inlet, the resulting profile is a ‘cone’ (Figure 1d–g) whose peak concentration and divergence are controlled with the MFCs (e.g. speeding up the odor flow while slowing down the airflow broadens the cone). Temporal gradients can be achieved by varying the odor flow in time, subject to constraints imposed by the odor’s absorption into the agar gel.

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The outflux from the flow chamber is connected to a flow meter and a photo-ionization detector (PID) to monitor the overall flow rate and odor concentration respectively. The geometry of this flow chamber is shown in Figure 1b, where parallel tubings are connected from the side and the chamber is vacuum sealed with a piece of acrylic on top during experiments. The chamber is designed for use with ∼100 mm square agar plates. The extra width (2.5 cm on either side) diminishes the influence of the chamber boundary on the odor profile over the arena. Interchangeable inserts allow for different agar substrates (e.g. circular plates) or for full calibration by odor sensor arrays (OSAs) (Figure 1c), discussed in the next section. Metal components are designed for low-cost fabrication by automated mechanisms (either laser cut-able or three-axis CNC machinable). The fabrication plans for the flow chamber and inserts (Data sharing), list of required components (Table 1, Supplementary file 2), and a detailed tutorial (Supplementary file 1) are all publicly available.

Table 1

Item	Manufacturer	Part number	Price estimate ($/unit)
Mass flow controller	Aalborg	GFCS-010654	$900
Photo-ionization detector	Ametek	piD-TECH	$500
Metal-oxide sensors	Sensirion	SGP30	$10
PCB board	MacroFab and OSH Park	Customized	$400
Flow chamber	eMachineShop	Customized	$250
Flow meter	Aalborg	Model P flow tube meters	$160
Teensy	PJRC	Teensy 4.0	$25
Camera	Basler	acA4112-30um	$3000
Bubbler	Sigma-Aldrich	Duran GL 45 connection	$200
Tubing connection	idex-upchurch	Fitting, manifold, and tubings	∼$1000

Measuring the spatiotemporal odor distributions

A central difficulty in measuring animals’ responses to olfactory cues is quantifying airborne odor concentrations that vary in space and time (Vickers and Baker, 1994; Tariq et al., 2021; Kadakia et al., 2022; Yamazoe-Umemoto et al., 2018). This difficulty is exacerbated in turbulent environments where odor plumes carry abrupt spatial and temporal jumps in concentration far from the source with fundamentally unpredictable dynamics (Celani et al., 2014; Dennler et al., 2022). But even in laminar flows, like those considered here, boundary conditions, slight changes in temperature, and the presence of absorbing substrates like agar make this challenging.

There is therefore a need, even for quasi-stationary gradients, to characterize the odor profiles in situ and to monitor these profiles during experiments. Various optical techniques, like laser-induced fluorescence or optical absorption (Louis et al., 2008; Tadres et al., 2022; Demir et al., 2020), exist to monitor concentration across planar arenas, but these have not been demonstrated for behavior experiments, are expensive to construct, require specially designed arenas, or some combination of these disadvantages. Electronic chemical sensors can reveal the time-varying concentration at a particular point in space. A tiled array of these sensors acts as a ‘camera’ forming a two-dimensional (2D) spatiotemporal reading of the concentration. The gold standard for measurement of odor concentration is the PID, but even the smallest versions of these sensors are both too large (∼2 cm in all dimensions) and too expensive (∼$500 each) to make an array. Metal-oxide odor sensors, designed to be used in commercial air quality sensors, are available in inexpensive and compact integrated circuit packages (Schmuker et al., 2016; Burgués et al., 2020). However, in general, commercial metal-oxide sensors are not designed for precision work - they tend to drift due to variations in heater temperature, humidity, adsorption of chemicals, and aging effects (Dennler et al., 2022). Most such sensors are designed to detect the presence of gas above a particular concentration but not to precisely measure the absolute concentration. We became aware of a newer metal-oxide sensor, the Sensirion SGP30 that was designed for long-term stability and concentration measurement; we wondered if such a sensor could be calibrated for use in an OSA.

To calibrate the sensor, we created a controllable concentration source by bubbling air through butanone. The odor reservoir contains butanone dissolved in water and is kept below the saturation concentration (11 mM or 110 mM odor sources). We then mixed this odorized airflow into a carrier stream of pure air. We kept the carrier airflow rate constant ($\sim 400$ mL/min) and varied the flow rate through the odor source ($0-50$ mL/min); the odor flow rate was slow enough that the vapor remained saturated, so the concentration of butanone in the mixed stream was proportional to the flow rate through the butanone bubbler, as directly measured with a PID (Figure 1—figure supplement 2). We typically calibrated concentration with continuously ramped flow rate in triangle waves with a 500 s period for 2–3 cycles. We performed calibrations across a range of odor concentrations and against two PIDs, each with a different dynamic range (0–200 ppm, Figure 1—figure supplement 1, 0–1000 ppm, Figure 1—figure supplement 3), and also compared against the PIDs to characterize the metal-oxide sensors’ temporal response (Figure 1—figure supplement 2). We found a one-to-one correspondence between the odor sensor reading and the PID reading that persisted over time and showed no obvious hysteresis. These metal-oxide sensors’ temporal response and stability appear to be well matched for capturing the slow fluctuations in our stable and non-turbulent odor landscapes. We also characterized the heating effects of these sensors in the arena (Figure 1—figure supplement 4), and conducted measurements to show that powering the sensors does not noticeably change the concentration of odorant in the chamber (Figure 1—figure supplement 3). We reasoned that after calibrating the metal-oxide sensors to a PID, we could use the array to measure spatiotemporal odor concentration distributions.

We constructed a sensor array from ‘odor sensor bars’ (OSBs), printed circuit boards each containing 16 sensors in two staggered rows of 8. Each OSB also contained 8 temperature and humidity sensors to allow compensation of the odor sensor readings. The OSBs are mounted orthogonal to the direction of airflow; 7 OSBs fit inside our flow chamber (112 sensors total) allowing a full measurement of the odor profile. Taken together these 112 sensors formed an OSA, capable of measuring odor concentrations with ∼1 cm spatial resolution sampled at 1 Hz. Prior to all experiments we calibrated the OSA in situ by varying the butanone concentration across the entire anticipated range of measurement while simultaneously recording the odor sensor and PID readings.

To verify the ability of the OSA to measure concentration gradients, we created an artificially simple steady-state odor landscape and compared it to our predictions based on a convection-diffusion flow model. We flowed in odorized air ($\sim 30$ mL/min) through a central tube and clean air through the others ($\sim 400$ mL/min distributed into 14 surrounding tubings) in an environment without agar and without animals. This results in an airflow velocity $\sim 5$ mm/s in the flow chamber. The odor concentration profile was established in a few minutes and remained stable over the timescale of 30 min behavioral experiments (Figure 1—figure supplement 5). As the flow rates and concentrations are all known and the flow is non-turbulent, and there is no agar or animals present, the concentration across the chamber should match a convection-diffusion model. After establishing the gradient, we recorded from the discrete odor sensors on the array (Figure 1d) and estimated the values in between sensors using spline interpolation with length scale equal to the inter-sensor distances (Figure 1e). We compared this stationary profile with a two-parameter convection-diffusion flow model (Figure 1f and g) fit to the data and described in the Materials and methods section. The measured concentrations in this artificially simplistic odor gradient show good agreement with the fit convection-diffusion model, especially in the central region where experiments are to be conducted, leading us to conclude that the OSA can accurately report the odor concentration. Note that the model serves as an independent validation of the sensors, and is not required to perform the measurement. We proceed to consider more complex odor environments.

We demonstrate several examples of flexible control over the odor profile. In the configuration shown in Figure 1, the center-most input provides odorized air and all surrounding inputs provide moisturized clean air to form a cone-shape stationary odor pattern. A narrower cone can be created by increasing the airflow (600 mL/min) of the surrounding inputs relative to the middle odorized flow (Figure 2a). The cone can be inverted by placing odorized air in the two most distal inputs, and clean air in all middle inputs (Figure 2b). This inverse cone has lower concentration in the middle and higher on the sides. In later animal experiments, we restrict odorized air to one side to form a biased-cone odor landscapes, resulting in a cone with an offset from the middle line of the arena. Many more configurations are possible, demonstrating that the odor flow chamber enables the flexible control of airborne odor landscapes that are much more complex than a single odor point source.

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Figure 2—source data 1

Physical properties of odorants.

Table odor_physics.xlsx in https://doi.org/10.6084/m9.figshare.21737303.

https://cdn.elifesciences.org/articles/85910/elife-85910-fig2-data1-v2.xlsx

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To demonstrate that the flow control and measurement methods are not restricted to any single odor molecule, we create and measure various stationary odor landscapes with benzaldehyde, ethanol, and ethyl butyrate odorants that, together with butanone, span a range of different vapor pressure and water solubility (Figure 2, Figure 2—source data 1). For each molecule, the metal-oxide odor sensors are separately calibrated against a PID (Figure 2—figure supplement 1). To our knowledge, most volatile organic compounds that can be sensed and calibrated by a PID should be compatible with our sensor array system. Depending on the vapor pressure, molecular weight of different molecules, and the geometry of our flow chamber, the cone-shape odor profiles can be skewed or less smooth. The skewed profile is possibly a result of the inhomogeneity in the pocket created by the odor sensor hub. Nonetheless, we empirically verified that these odor profiles are stationary and reproducible.

Odor-agar interactions dominate classical butanone droplet assays

Classic chemotaxis experiments in small animals commonly construct odor environments with odor droplets in a Petri dish, usually with a substrate such as agar. Our odor delivery instrument is designed to be compatible with a similar environment. To first better understand classical chemotaxis experiments with butanone, we sought to characterize the spatiotemporal odor profile from an odor droplet point source using our OSA. We first considered the case without agar. In that case the odor concentration should be governed entirely by gas phase diffusion. We placed a 2 µL droplet of 10% butanone in water on the lid of our instrument centered in the arena above the full OSA and without any airflow (Figure 3a and b). Butanone was observed to diffuse across the arena in the first 3 min (Supplementary Video; Figure 3—video 1) resulting in an equilibrium concentration that was close to uniform across the odor sensors. We note that the final concentration of roughly 100 ppm and the equilibration timescale both match what we would expect from first principals for $\sim {10}^{-6}$ mol of butanone in an ${\displaystyle \sim 225\mathrm{m}\mathrm{L}}$ arena that have a diffusion rate of ${\displaystyle \sim 0.08{cm}^2/\mathrm{s}}$ in air. A uniform odor landscape is not helpful for studying odor-guided navigation, but most behavioral experiments are not conducted in a bare flow chamber but contain a biologically compatible substrate, such as an agar gel, as is typically used in droplet assays. We therefore sought to investigate the role that agar plays in sculpting the odor landscape in butanone droplet experiments.

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We introduced agar into the droplet assay by removing two sensor bars and replacing them with agar. We placed a butanone droplet directly on the agar or on the lid, as done classically in worm and larval assays respectively, and measured the odor landscape over time (Figure 3b and c). The odor concentration measured with agar is dramatically different from that measured without agar. Instead of quickly equilibrating to a uniform concentration, in the presence of agar there was instead a local maximum of butanone surrounding the droplet that persists even after 3 min. This difference in airborne odor concentration with and without agar persists after experimental perturbation such as removing and replacing the lid over the chamber (Figure 3—figure supplement 1). This convinced us of the need to account for odor-agar interactions when working with water-soluble odorants such as butane.

Agar poses challenges for establishing stable odor landscapes

By placing the agar on the underside of the lid hanging above the odor sensors, we were able to simultaneously record spatial odor concentrations generated by our flow chamber opposite the surface of the agar during animal behavior (Figure 1—figure supplement 6). Unfortunately, when imaging in this configuration, the odor sensors in the background obstruct the animals from being tracked over the extent of the arena. We therefore sought an alternative strategy: we chose to measure the odor profile along only the boundary of the arena in order to infer the odor concentration inside the arena in such a way as to also take into account the odor-agar interactions. This approach relies on establishing temporally stable odor landscapes, even in the presence of agar, which we sought to accomplish by leveraging our ability to control the flow of odor.

We first sought to measure how the presence of agar altered the odor profiles that would have been established by flow in a bare chamber lacking agar (Figure 1, Figure 2). As in Figure 3c and d, we replaced two OSBs with a rectangular strip of agar gel (‘w/ agar’) or a metal plate as a control (‘w/o agar’), and then measured airborne odor concentration upstream and downstream of the agar under odorized airflow that would normally produce a cone profile (Figure 4a–b). While the agar had little effect on the odor landscape upstream of the agar, it drastically altered the downstream odor landscape (Figure 4b), suggesting that the agar absorbs the airborne butanone molecules. This finding is consistent with the odor droplet experiments (Figure 3), repeated with a full-sized 96 mm square agar plate intended for use with animals (Figure 4c), and to be expected since butanone is highly soluble in water (275 g/L). Pulse-chase style experiments confirm that agar does indeed absorb and reemit butanone (Figure 4—figure supplement 1b). To accommodate the full-sized agar plate, we measured only the 1D odor profiles upstream and downstream of the agar (Figure 4d). Taken together, these experiments suggest that agar-butanone interaction presents a challenge for setting up and maintaining stable butanone odor landscapes.

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Monitoring the boundary determines the odor landscape at quasi-equilibrium

Pre-equilibration allows us to quickly establish a temporally stable odor landscape even in the presence of agar, and stability is indicated by monitoring the odor sensors along the boundary. Now we seek an approach to infer the odor concentration over the agar from the measurements along the boundary. In this section we will show that the odor concentration above the agar after pre-equilibration should be the same as the odor concentration when no agar is present and we will then validate this equivalence empirically (to within 10%). We will then use this knowledge to infer the odor concentration above the agar at quasi-equilibrium using the measurements along the boundary. If the airborne odor concentration along the boundaries upstream and downstream of the agar matches the profile in the absence of agar, one can infer that the airborne odor concentration landscape above the agar is also the same. We validated this approach in three ways: via analytical argument, via numeric simulation, and via direct empirical measurement.

The physical intuition is as follows: in the absence of sources or sinks, the fact that two odor concentration distributions obey the same differential equations and share the same conditions on all boundaries means that the distributions are identical throughout the interior. Agar adds complexity because it can act as a source or sink by absorbing and reemiting the odor, but even then, knowledge that the odor along the boundary is the same with and without agar places strong constraints on the interior odor landscape. The only way the interior could differ would be if sources and sinks of odor in the agar were precisely arranged so that all excess odor emitted from one point is exactly reabsorbed somewhere else before reaching the downstream boundary. Not only is such an arrangement unlikely, it is inherently temporally unstable and should tend to eliminate the excess sources and sinks over time. A mathematical version of this argument is presented in Appendix 1. Therefore, when we measure steady odor concentration along the boundary with agar that matches the concentration without, we are comfortable concluding that the odor landscape with agar is the same as without.

A numerical reaction-convection-diffusion model also shows that after pre-equilibration, the with-agar odor landscape matches the without-agar odor landscape (Figure 5). The model requires reasonable assumptions about the absorption rate, reemission rate, and capacity of the agar. The numerical simulation provides qualitative agreement to our empirical observations in Figure 4.

Figure 5

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We experimentally verified that the airborne odor concentration above the agar matched our expectations based on measurements made along the boundary, by performing direct measurements of the airborne odor opposite the agar. We used an upside-down configuration as in Figure 1—figure supplement 6 and Figure 6: we poured agar onto the lid and measured the odor concentration below it with the full sensor array underneath (Figure 6, Figure 1—figure supplement 6).

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After the pre-equilibrium protocol, measured airborne concentration in the arena with agar was similar to measurements made without agar to within an average of less than 10% fractional difference in the area that the animal would crawl (Figure 6b). The agreement of odor landscape measured with and without agar at quasi-equilibrium agrees with our modeling and indicates that the odor landscape is stable and identifiable in our setup by monitoring the odor profile along the boundary. Therefore, an experimenter could establish a desired odor gradient without agar, and then, even in the presence of agar, once the boundary measurements matched, the experimenter would be confident that the odor landscapes also matched. Note that our hypothetical experimenter would not be required to perform any simulations or calculations.

The presence of worms did not noticeably alter the odor concentration (Figure 6—figure supplement 1). Our approach relies on reaching quasi-steady state. In measurements made without the pre-equilibration protocol, and therefore presumably before quasi-steady state, the interior odor concentration with agar no longer matches the without agar, as expected (Figure 6c).

Butanone chemotaxis in C. elegans

We sought to use our experimental approach to directly quantify C. elegans’ navigation strategies for airborne butanone. C. elegans are known to climb gradients toward butanone (Cho et al., 2016; Levy and Bargmann, 2020). Microfluidic environments suggest that they use a biased random walk strategy to navigate in a liquid butanone environment (Levy and Bargmann, 2020; Albrecht and Bargmann, 2011). Worms are also known to use weathervaning to navigate airborne gradients of other odors (Iino and Yoshida, 2009; Kunitomo et al., 2013) although to our knowledge this has not been specifically investigated for butanone.

Worms were imaged crawling on agar in the flow chamber under an airborne butanone odor landscape illuminated by infrared light. Here, six recording assays were presented, with approximately 50-100 animals per assay, and two different odor landscapes were used. C. elegans navigated up the odor gradient toward higher concentrations of butanone, as expected (Figure 7a and b). Importantly, the odor concentration experienced by the animal at every point in time was inferred from concurrent measurements of the odor profile along the boundary of the agar (Figure 7c). On average, animals were more likely to travel in a direction up the local gradient than away from the local gradient, as expected for chemo-attraction (Figure 7d). We use the term ‘bearing to local gradient’ to describe the animal’s direction of travel with respect to the local odor gradient that it experiences.

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We find quantitative evidence that the worm exhibits both biased random walk and weathervaning strategies. To investigate biased random walks, we measured the animal’s probability of turning (pirouette) depending on its bearing with respect to the local airborne butanone gradient (Figure 7e). We find that the animal is least likely to turn when it navigates up the local gradient and most likely to turn when it navigates down the gradient, a key signature of the biased random walk strategy (Berg, 2018; Mattingly et al., 2021).

To test for weathervaning, we measured how the curvature of the animal’s trajectory depended on its bearing with respect to the local airborne butanone gradient (Figure 7f). When the animal’s navigation aligns to the axis of the butanone gradient, the distribution of the curvature of its trajectory was roughly symmetric and centered around 0 (straight line trajectory). By contrast, when the animal navigated more perpendicular to the gradient, the distribution of the curvature of its trajectories was skewed. The skew was such that it enriched for cases where the animal curved its trajectories toward the local gradient. We further calculate the average curvature as a function of bearing to local gradients following methods in Iino and Yoshida, 2009; Figure 7—figure supplement 2a. The result shows that the worm on average turns toward the gradient direction, which is the key signature of weathervaning strategy. Both the biased random walk and weathervaning behavior was absent in control experiments with flow but not odor, and we observed no evidence of anemotaxis at the $\sim 5$ mm/s air velocities encountered by the animals (Figure 7—figure supplement 1, Figure 7—figure supplement 2c). We conclude that C. elegans utilize both biased random walk and weathervaning strategies to navigate butanone airborne odor landscapes (Iino and Yoshida, 2009; Yoshida et al., 2012). Both strategies had been observed for salt chemotaxis before, but to our knowledge had not previously been quantitatively characterized for airborne butanone. More broadly, previous measurements of navigation in response to airborne odors for C. elegans have not had access to the underlying odor concentration and therefore could only use proxies, such as the bearing angle or distance with respect to a point odor source. Our approach allows a more direct description of the navigation strategy because we characterize turn probabilities and other features with respect to the local gradient directly (Figure 7), as opposed to a proxy.

To identify potential neurons involved in mediating the navigation strategy, we measured butanone-guided navigation of mutant animals in which the interneuron AIB was chronically inhibited. Disruptions to AIB had previously been shown to either increase weathervaning (AIB ablation) (Iino and Yoshida, 2009) or decrease biased random walks (Luo et al., 2014) (AIB inhibition) when navigating in a salt gradient. But to our knowledge, AIB’s role in influencing the animal’s butanone-guided navigation strategy had not previously been measured. We observed that AIB-inhibited animals still climbed butanone gradients, but the worm relied on different behavioral strategies (Figure 8). The turn probability was still biased by the concentration gradient suggesting that the animal still used a biased random walk strategy, although turning rate was overall lower. Most dramatically, the animal showed down-regulated weathervaning. In wild-type (N2) animals the distribution of the curvature of the animal’s trajectory depends on its orientation to the local odor gradient (Figure 7f, Figure 7—figure supplement 2a) indicating weathervaning, but in AIB-inhibited animals the distribution of trajectory curvature is much less dependent on orientation to the local gradient (Figure 8d, Figure 7—figure supplement 2b). Together this suggests that AIB is important for supporting turning rate and the weathervaning strategy, and demonstrates the potential for this approach to characterize mutants or investigate neural circuits underlying navigation.

Figure 8

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Having access to quantitative information about the odor the animal experiences provides an opportunity to characterize the animal’s overall gradient climbing performance through observations of its local movements on a local gradient (Mattingly et al., 2021). We compute the animal’s drift velocity as a function of local gradient steepness (Figure 9). This captures the animal’s overall gradient climbing performance as a result of all the navigational strategies it uses, including the biased random walk and weathervaning. The analysis provides information relevant to a chemotaxis index, but it has the additional benefit of describing quantitatively how the animal responds to a local gradient and on what timescale. This analysis is only possible with a knowledge of the odor concentration experienced by the animal, and is naturally accessible in our instrument.

Figure 9

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Butanone chemotaxis in Drosophila larvae

To further evaluate the utility of the flow chamber for the study of small animal navigation, we investigated how larval Drosophila navigate butanone. Although butanone is not as commonly used as a stimulus with Drosophila as with C. elegans, butanone is known to be attractive to larval flies (Dubin et al., 1995; Dubin et al., 1998) and has been variously reported to be attractive (Park et al., 2002; Jung et al., 2015) and aversive (Israel et al., 2022; Lerner et al., 2020) to adult flies. To investigate the larva’s navigational strategy in a butanone gradient, we created a ‘cone’-shaped butanone gradient over the agar substrate using the pre-equilibration protocol, as before, and we confirmed the presence and stability of the gradient by continuously measuring the spatial distribution of butanone upstream and downstream of the agar arena. We monitored the orientation and movement of 59 larvae over six separate 10 min experiments (∼ 10 larvae per experiment) with an average observation time of 7 min per larva (Figure 10).

Figure 10

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Larvae moved toward higher concentration of butanone (Figure 10a). To analyze the strategy by which they achieved this, we first constructed a coordinate system in which 0° was in the direction of the odor gradient (toward higher concentration) and 180° was directly down-gradient; angles increased counterclockwise when viewed from above. We found that larvae initiated turns at a higher rate when headed down-gradient ($\pm {180}^{\circ }$ bearing with respect to the local gradient) than up-gradient ($0^{\circ }$) (Figure 10b). When larvae turned, their reorientations tended to orient up-gradient (negative angle changes from $+{90}^{\circ }$ bearing with respect to the local gradient, and positive angle changes from $-{90}^{\circ }$) (Figure 10c). Bias in turning rate and reorientations in larvae are known navigation strategies in larvae (Gershow et al., 2012; Gomez-Marin et al., 2011; Gomez-Marin and Louis, 2014), but here it is to our knowledge the first characterization for butanone. With modulation of turning rate and orientation in the same odor environment, we conclude that Drosophila larvae use similar navigational strategies to C. elegans to move toward butanone. We note that the chemotaxis efficiency is much higher in larvae compared to worms given the order of magnitude difference in run velocity (∼3 mm/s in larvae and ∼0.1 mm/s in worms).

We present a custom-designed flow chamber and OSA that enables us to measure navigation strategies of worms and fly larvae within the context of a controlled and measured odor environment. The key features of this odor delivery system are that (1) the odor concentration profile through space is controlled in the flow chamber, (2) the OSA provides a spatial readout to calibrate and measure the profile, and (3) the odor concentration profile is monitored during animal experiments. This last feature, the ability to monitor the spatial profile of odor concentration on the boundary during experiments, to our knowledge sets this method apart from previous approaches.

The odor landscape experienced by the animal can be recovered by one of two ways, with different tradeoffs. Typically, for a given set of flow parameters, we first quantify the odor gradient using the whole sensor array when agar is absent, then during behavioral experiments, we confirm the desired odor profile is achieved using sensor bars mounted along the boundary upstream and downstream of agar. During experiments with agar, the real-time monitoring of the odor profile at the edges of the arena guards against a range of possible errors or experimental imperfections. For example, exhaustion of the odor source, blocked connections, imperfect seals, and so on will all be detected by a change in the odor profile along the boundary, thereby avoiding silent changes to the odor landscape that may skew behavioral results. Indeed, it was this real-time monitoring that first alerted us to the fact that absorption of odor into the agar substrate caused the odor landscape to differ from our naive expectations.

If real-time monitoring of the entire arena during behavioral experiments is desired, the odor array can instead be mounted opposite the agar surface (Figure 6). We show that this introduces complications in imaging and makes tracking animals challenging (Figure 1—figure supplement 6). Future work could address these challenges through alternate lighting schemes (e.g. frustrated TIR in Risse et al., 2013) or by masking the sensor array with a darker material. For most applications, however, we expect that confirming the profile via sensors along the boundary will be sufficient because we have demonstrated that the odor landscape established by flow at quasi-equilibrium is stable and repeatable in the absence of experimenter or instrument errors (and detectable when not).

The ability to monitor spatial profile during experiments, along with a quantitative understanding of odor-agar interactions, provides confident knowledge of the odor experienced by the animal over time. This is especially important when we analyze time-varying navigation behavior in an odor environment that potentially changes in time too (Yamazoe-Umemoto et al., 2018; Yoshida et al., 2012; Levy and Bargmann, 2020). Past work focusing on neural circuit mechanisms for odor navigation in worms have often relied on coarser readout such as the chemotaxis index or using the distance to odor source as a proxy for the inverse of odor concentration. However, it has been shown that the exact odor concentration alters the animal’s behavioral response to odor (Yoshida et al., 2012; Levy and Bargmann, 2020). Quantifying tuning curves with respect to more precise odor measurements may better constrain the investigations into neural mechanisms driving the sensorimotor transformations underlying navigation algorithms (Clark et al., 2013; Liu et al., 2020).

In contrast to liquid delivery of odor gradients via microfluidic chips (Albrecht and Bargmann, 2011; Larsch et al., 2015), our method allows worms to crawl freely on an agar surface. This allows our behavior measurements to be directly compared against classical chemotaxis assays (Louis et al., 2008; Pierce-Shimomura et al., 1999). Additionally, the macroscopic odor airflow chamber makes it straightforward to flexibly adjust the spatial pattern between experiments without the need to redesign the chamber. A detailed tutorial for assembling the instrument and the protocol for experiments is provided in Supplementary file 1.

Our setup uses low flow rates corresponding to low wind speed velocities (5 mm/s) to avoid anemotaxis. Larger organisms, including adult flies, navigate toward odor sources by combining odor and wind flow measurements (Vergassola et al., 2007; Matheson et al., 2022). Fly larvae exhibit negative anemotaxis at wind speeds 200–1000 times higher than those used here (Jovanic et al., 2019), but previous work showed that they do not exhibit anemotaxis at lower wind speeds like the ones used here (Gershow et al., 2012). For example, they do not exhibit anemotaxis at 12 mm/s which is still higher than the velocities they experience here (Gershow et al., 2012). Therefore, we do not expect Drosophila larvae to exhibit anemotaxis under our flow conditions. C. elegans are not thought to respond to airflow. In experiments in aqueous microfluidic chips under flow, C. elegans move toward higher concentrations of attractant and do not respond to the flow of the liquid (Albrecht and Bargmann, 2011). In agreement, we do not observe any evidence of C. elegans anemotaxis in our chamber in response to control experiments without odor and with wind speeds of 5 mm/s (Figure 7—figure supplement 1).

We focused on the odor butanone because it is important for a prominent associative learning assay (Torayama et al., 2007; Kauffman et al., 2011). Butanone has high vapor pressure and is highly soluble in water and therefore it interacts strongly with agar. In this work we show that this odor-agar interaction makes it difficult to a priori infer an odor landscape experienced by the animal when agar is present, but that continuously monitoring the odor profile on the boundary overcomes this challenge.

Odorants with less solubility and lower vapor pressure may not require pre-equilibration to reach quasi-equilibrium quickly, but instead may be able to stabilize across agar on a shorter timescale. For example, ethyl butyrate is less water soluble (∼0.586 g/100 mL in water, compared to 27.5 g/100 mL for butanone), has lower pressure (11.3 mmHg compared to 78 mmHg for butanone), and has a smaller gas phase diffusion coefficient (5.6 × ${10}^{-3}$ cm²/s compared to ∼69 cm²/s for butanone). A droplet of ethyl butyrate has been shown to form a relatively stable odor landscape over a 5 min timescale in a dish on agar without flow (Tadres et al., 2022; Louis et al., 2008). Here, we demonstrate stable odor landscapes over longer timescales of at least 30 min with a more volatile odor, butanone, by using our flow-based assay (Figure 1—figure supplement 5). A strength of the flow-based approach with monitoring is that it should, in principle, allow for the use of a wider range of odors. Crucially, the continuous monitoring makes this approach more robust against other unforeseen odor interactions, such as interactions between an odorant and glass, aluminum or plastic.

Here, we have addressed the problem of creating non-turbulent airborne odor landscapes. The biophysical processes governing odor sensing in small animals such as C. elegans are not fully understood. The worm carries a thin layer of moisture around its body as it moves on the agar substrate (Bargmann, 2006) and it is unclear to what extent the worm pays attention to the concentration of an odorant in the agar below it vs the air above it. Our reaction-convection-diffusion model suggests that at the quasi-equilibrium conditions used in our experiments the odor concentration in agar is related to the airborne odor concentration directly above it up to a scalar that we predict to be constant across the agar. Although we have not measured this empirically, this suggests that even in the extreme case that the animal only senses odor molecules in the agar, the odor concentration experienced by the animal in our experiments should differ by no more than a scaling factor compared to our estimates based on the airborne odor concentration.

Here, we also showed the possibility of and challenges associated with directly measuring the full spatial odor profile (instead of using the boundary) during simultaneous behavior tracking (Figure 1—figure supplement 6). Future work is needed to improve concurrent animal tracking methods, to design more miniaturized and scalable sensors, and to generalize to more complex or 3D environments.

Knowing the concentration experienced by the animal is not only useful for measuring navigational strategies more precisely than in classical assays, like the droplet chemotaxis assays. It will also be crucial for studying changes in navigational strategy, such as those in the context of associative learning (Cho et al., 2016; Torayama et al., 2007), sensory adaptation (Levy and Bargmann, 2020; Itskovits et al., 2018), and long timescale behavioral states (Calhoun et al., 2014; Gomez-Marin et al., 2011; Klein et al., 2017). In all those cases, it will be critical to disambiguate slight changes to the odor landscape from gradual changes in the navigational strategies. Continuously monitoring the odor landscape during behavior will remove this ambiguity.

Recordings for odor flow control, concentration measurements, and behavioral tracking data are publicly available: https://doi.org/10.6084/m9.figshare.21737303. Compressed 'zip' archive Chen_flow_20222.zip contains raw data. Compressed 'zip' archive flow_chamber_machined_components.zip contains CAD and other design files related to the hardware.

1. 1. Chen K
   2. Gershow M
   3. Leifer A

   (2023) figshare

   Recordings for odor flow control, concentration measurements, and behavioral tracking data.

   https://doi.org/10.6084/m9.figshare.21737303

1. 1. Aceves-Piña EO
   2. Quinn WG

   (1979) Learning in normal and mutant Drosophila larvae

   Science 206:93–96.

   https://doi.org/10.1126/science.206.4414.93

   • PubMed
   • Google Scholar
2. 1. Albrecht DR
   2. Bargmann CI

   (2011) High-content behavioral analysis of Caenorhabditis elegans in precise spatiotemporal chemical environments

   Nature Methods 8:599–605.

   https://doi.org/10.1038/nmeth.1630

   • PubMed
   • Google Scholar
3. 1. Baker KL
   2. Dickinson M
   3. Findley TM
   4. Gire DH
   5. Louis M
   6. Suver MP
   7. Verhagen JV
   8. Nagel KI
   9. Smear MC

   (2018) Algorithms for olfactory search across species

   The Journal of Neuroscience 38:9383–9389.

   https://doi.org/10.1523/JNEUROSCI.1668-18.2018

   • PubMed
   • Google Scholar
4. 1. Bargmann CI
   2. Horvitz HR

   (1991) Chemosensory neurons with overlapping functions direct chemotaxis to multiple chemicals in C. elegans

   Neuron 7:729–742.

   https://doi.org/10.1016/0896-6273(91)90276-6

   • PubMed
   • Google Scholar
5. 1. Bargmann CI
   2. Hartwieg E
   3. Horvitz HR

   (1993) Odorant-selective genes and neurons mediate olfaction in C. elegans

   Cell 74:515–527.

   https://doi.org/10.1016/0092-8674(93)80053-h

   • PubMed
   • Google Scholar
6. 1. Bargmann CI

   (2006) Chemosensation in C. elegans

   WormBook 1:1–29.

   https://doi.org/10.1895/wormbook.1.123.1

   • PubMed
   • Google Scholar
7. 1. Berg HC
   2. Brown DA

   (1972) Chemotaxis in Escherichia coli analysed by Three-dimensional Tracking

   Nature 239:500–504.

   https://doi.org/10.1038/239500a0

   • Google Scholar
8. Book

   1. Berg HC

   (2018) Random Walks in Biology

   Princeton University Press.

   https://doi.org/10.2307/j.ctv7r40w6

   • Google Scholar
9. 1. Boie SD
   2. Connor EG
   3. McHugh M
   4. Nagel KI
   5. Ermentrout GB
   6. Crimaldi JP
   7. Victor JD

   (2018) Information-theoretic analysis of realistic odor plumes: What cues are useful for determining location?

   PLOS Computational Biology 14:e1006275.

   https://doi.org/10.1371/journal.pcbi.1006275

   • PubMed
   • Google Scholar
10. 1. Burgués J
    2. Hernández V
    3. Lilienthal AJ
    4. Marco S

    (2020) Gas distribution mapping and source localization using a 3D grid of metal oxide semiconductor sensors

    Sensors and Actuators B 304:127309.

    https://doi.org/10.1016/j.snb.2019.127309

    • Google Scholar
11. 1. Calhoun AJ
    2. Chalasani SH
    3. Sharpee TO

    (2014) Maximally informative foraging by Caenorhabditis elegans

    eLife 3:e04220.

    https://doi.org/10.7554/eLife.04220

    • PubMed
    • Google Scholar
12. 1. Calhoun AJ
    2. Murthy M

    (2017) Quantifying behavior to solve sensorimotor transformations: advances from worms and flies

    Current Opinion in Neurobiology 46:90–98.

    https://doi.org/10.1016/j.conb.2017.08.006

    • PubMed
    • Google Scholar
13. 1. Celani A
    2. Villermaux E
    3. Vergassola M

    (2014) Odor landscapes in turbulent environments

    Physical Review X 4:041015.

    https://doi.org/10.1103/PhysRevX.4.041015

    • Google Scholar
14. 1. Cho CE
    2. Brueggemann C
    3. L’Etoile ND
    4. Bargmann CI

    (2016) Parallel encoding of sensory history and behavioral preference during Caenorhabditis elegans olfactory learning

    eLife 5:e14000.

    https://doi.org/10.7554/eLife.14000

    • PubMed
    • Google Scholar
15. 1. Chronis N
    2. Zimmer M
    3. Bargmann CI

    (2007) Microfluidics for in vivo imaging of neuronal and behavioral activity in Caenorhabditis elegans

    Nature Methods 4:727–731.

    https://doi.org/10.1038/nmeth1075

    • PubMed
    • Google Scholar
16. 1. Clark DA
    2. Freifeld L
    3. Clandinin TR

    (2013) Mapping and cracking sensorimotor circuits in genetic model organisms

    Neuron 78:583–595.

    https://doi.org/10.1016/j.neuron.2013.05.006

    • PubMed
    • Google Scholar
17. 1. Coen P
    2. Clemens J
    3. Weinstein AJ
    4. Pacheco DA
    5. Deng Y
    6. Murthy M

    (2014) Dynamic sensory cues shape song structure in Drosophila

    Nature 507:233–237.

    https://doi.org/10.1038/nature13131

    • PubMed
    • Google Scholar
18. Conference

    1. Dahlberg BA
    2. Izquierdo EJ

    (2020)

    Contributions from parallel strategies for spatial orientation

    The 2020 Conference on Artificial Life.

    • Google Scholar
19. 1. Demir M
    2. Kadakia N
    3. Anderson HD
    4. Clark DA
    5. Emonet T

    (2020) Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters

    eLife 9:e57524.

    https://doi.org/10.7554/eLife.57524

    • PubMed
    • Google Scholar
20. 1. Dennler N
    2. Rastogi S
    3. Fonollosa J
    4. van Schaik A
    5. Schmuker M

    (2022) Drift in a popular metal oxide sensor dataset reveals limitations for gas classification benchmarks

    Sensors and Actuators B 361:131668.

    https://doi.org/10.1016/j.snb.2022.131668

    • Google Scholar
21. 1. Drix D
    2. Schmuker M

    (2021) Resolving fast gas transients with metal oxide sensors

    ACS Sensors 6:688–692.

    https://doi.org/10.1021/acssensors.0c02006

    • PubMed
    • Google Scholar
22. 1. Dubin AE
    2. Heald NL
    3. Cleveland B
    4. Carlson JR
    5. Harris GL

    (1995) Scutoid mutation of Drosophila melanogaster specifically decreases olfactory responses to short-chain acetate esters and ketones

    Journal of Neurobiology 28:214–233.

    https://doi.org/10.1002/neu.480280208

    • PubMed
    • Google Scholar
23. 1. Dubin A.E
    2. Liles MM
    3. Seligman F
    4. Le T
    5. Tolli J
    6. Harris GL

    (1998) Involvement of genes encoding a K+ channel (ether a go-go) and a Na+ channel (smellblind) in Drosophila olfaction

    Annals of the New York Academy of Sciences 855:212–222.

    https://doi.org/10.1111/j.1749-6632.1998.tb10569.x

    • PubMed
    • Google Scholar
24. 1. Evans TC

    (2006) Transformation and microinjection

    WormBook 10:450.

    https://doi.org/10.1895/wormbook.1.108.1

    • Google Scholar
25. 1. Gepner R
    2. Mihovilovic Skanata M
    3. Bernat NM
    4. Kaplow M
    5. Gershow M

    (2015) Computations underlying Drosophila photo-taxis, odor-taxis, and multi-sensory integration

    eLife 4:e06229.

    https://doi.org/10.7554/eLife.06229

    • PubMed
    • Google Scholar
26. 1. Gershow M
    2. Berck M
    3. Mathew D
    4. Luo L
    5. Kane EA
    6. Carlson JR
    7. Samuel ADT

    (2012) Controlling airborne cues to study small animal navigation

    Nature Methods 9:290–296.

    https://doi.org/10.1038/nmeth.1853

    • Google Scholar
27. Software

    1. GershowLab

    (2023a) OdorSensorArray, version swh:1:rev:d290dc2f878919c4a13e3e4528ff50c6de34563d

    Github.

    https://archive.softwareheritage.org/swh:1:dir:ff0935296df626ff9698f3852829751ea5bd1b4c;origin=https://github.com/GershowLab/OdorSensorArray;visit=swh:1:snp:b0195c9db5fbb265a93d2c2f86a62ca6d22fed38;anchor=swh:1:rev:d290dc2f878919c4a13e3e4528ff50c6de34563d
28. Software

    1. GershowLab

    (2023b) Chen-Wu-eLife-Drosophila-image-capture, version swh:1:rev:9e3a0e7b341243b0c32c4f352bae681aa30966b4

    Software Heritage.

    https://archive.softwareheritage.org/swh:1:dir:bb4b2b8d00ab4ed0ce5a3457b4b22a633a6c6f75;origin=https://github.com/GershowLab/Chen-Wu-eLife-Drosophila-Image-Capture;visit=swh:1:snp:c38fc090078907f9256a01b5d4a0e90136e1614b;anchor=swh:1:rev:9e3a0e7b341243b0c32c4f352bae681aa30966b4
29. 1. Gomez-Marin A
    2. Stephens GJ
    3. Louis M

    (2011) Active sampling and decision making in Drosophila chemotaxis

    Nature Communications 2:441.

    https://doi.org/10.1038/ncomms1455

    • PubMed
    • Google Scholar
30. 1. Gomez-Marin A
    2. Louis M

    (2014) Multilevel control of run orientation in Drosophila larval chemotaxis

    Frontiers in Behavioral Neuroscience 8:38.

    https://doi.org/10.3389/fnbeh.2014.00038

    • PubMed
    • Google Scholar
31. 1. Gorur-Shandilya S
    2. Martelli C
    3. Demir M
    4. Emonet T

    (2019) Controlling and measuring dynamic odorant stimuli in the laboratory

    The Journal of Experimental Biology 222:jeb207787.

    https://doi.org/10.1242/jeb.207787

    • PubMed
    • Google Scholar
32. 1. Iino Y
    2. Yoshida K

    (2009) Parallel use of two behavioral mechanisms for chemotaxis in Caenorhabditis elegans

    The Journal of Neuroscience 29:5370–5380.

    https://doi.org/10.1523/JNEUROSCI.3633-08.2009

    • PubMed
    • Google Scholar
33. 1. Israel S
    2. Rozenfeld E
    3. Weber D
    4. Huetteroth W
    5. Parnas M

    (2022) Olfactory stimuli and moonwalker SEZ neurons can drive backward locomotion in Drosophila

    Current Biology 32:1131–1149.

    https://doi.org/10.1016/j.cub.2022.01.035

    • PubMed
    • Google Scholar
34. 1. Itskovits E
    2. Ruach R
    3. Kazakov A
    4. Zaslaver A

    (2018) Concerted pulsatile and graded neural dynamics enables efficient chemotaxis in C. elegans

    Nature Communications 9:2866.

    https://doi.org/10.1038/s41467-018-05151-2

    • PubMed
    • Google Scholar
35. 1. Izquierdo EJ
    2. Williams PL
    3. Beer RD

    (2015) Information Flow through a Model of the C. elegans Klinotaxis Circuit

    PLOS ONE 10:e0140397.

    https://doi.org/10.1371/journal.pone.0140397

    • PubMed
    • Google Scholar
36. 1. Jovanic T
    2. Winding M
    3. Cardona A
    4. Truman JW
    5. Gershow M
    6. Zlatic M

    (2019) Neural Substrates of Drosophila Larval Anemotaxis

    Current Biology 29:554–566.

    https://doi.org/10.1016/j.cub.2019.01.009

    • PubMed
    • Google Scholar
37. 1. Jung SH
    2. Hueston C
    3. Bhandawat V

    (2015) Odor-identity dependent motor programs underlie behavioral responses to odors

    eLife 4:e11092.

    https://doi.org/10.7554/eLife.11092

    • PubMed
    • Google Scholar
38. 1. Kadakia N
    2. Demir M
    3. Michaelis BT
    4. DeAngelis BD
    5. Reidenbach MA
    6. Clark DA
    7. Emonet T

    (2022) Odour motion sensing enhances navigation of complex plumes

    Nature 611:754–761.

    https://doi.org/10.1038/s41586-022-05423-4

    • PubMed
    • Google Scholar
39. 1. Kauffman A
    2. Parsons L
    3. Stein G
    4. Wills A
    5. Kaletsky R
    6. Murphy C

    (2011) C. elegans positive butanone learning, short-term, and long-term associative memory assays

    Journal of Visualized Experiments 11:2490.

    https://doi.org/10.3791/2490

    • PubMed
    • Google Scholar
40. Software

    1. Kevin-SC

    (2023) Leifer-behavior-triggered-averaging-Tracker-new, version swh:1:rev:f7e33d743b02d085e3f2413d0e3d024ae8fb5626

    Github.

    https://archive.softwareheritage.org/swh:1:dir:53ccf21365e6957660aeac39e8115f969356fa15;origin=https://github.com/Kevin-Sean-Chen/leifer-Behavior-Triggered-Averaging-Tracker-new;visit=swh:1:snp:169a87d71d227b3e12683f35261a0677c198c191;anchor=swh:1:rev:f7e33d743b02d085e3f2413d0e3d024ae8fb5626
41. 1. Klein M
    2. Krivov SV
    3. Ferrer AJ
    4. Luo L
    5. Samuel AD
    6. Karplus M

    (2017) Exploratory search during directed navigation in C. elegans and Drosophila larva

    eLife 6:e30503.

    https://doi.org/10.7554/eLife.30503

    • Google Scholar
42. 1. Kunitomo H
    2. Sato H
    3. Iwata R
    4. Satoh Y
    5. Ohno H
    6. Yamada K
    7. Iino Y

    (2013) Concentration memory-dependent synaptic plasticity of a taste circuit regulates salt concentration chemotaxis in Caenorhabditis elegans

    Nature Communications 4:1–11.

    https://doi.org/10.1038/ncomms3210

    • PubMed
    • Google Scholar
43. 1. Larsch J
    2. Flavell SW
    3. Liu Q
    4. Gordus A
    5. Albrecht DR
    6. Bargmann CI

    (2015) A Circuit for Gradient Climbing in C. elegans Chemotaxis

    Cell Reports 12:1748–1760.

    https://doi.org/10.1016/j.celrep.2015.08.032

    • PubMed
    • Google Scholar
44. 1. Lerner H
    2. Rozenfeld E
    3. Rozenman B
    4. Huetteroth W
    5. Parnas M

    (2020) Differential Role for a Defined Lateral Horn Neuron Subset in Naïve Odor Valence in Drosophila

    Scientific Reports 10:6147.

    https://doi.org/10.1038/s41598-020-63169-3

    • PubMed
    • Google Scholar
45. 1. Levy S
    2. Bargmann CI

    (2020) An adaptive-threshold mechanism for odor sensation and animal navigation

    Neuron 105:534–548.

    https://doi.org/10.1016/j.neuron.2019.10.034

    • PubMed
    • Google Scholar
46. 1. Liu M
    2. Sharma AK
    3. Shaevitz JW
    4. Leifer AM

    (2018) Temporal processing and context dependency in Caenorhabditis elegans response to mechanosensation

    eLife 7:e36419.

    https://doi.org/10.7554/eLife.36419

    • PubMed
    • Google Scholar
47. 1. Liu A
    2. Papale AE
    3. Hengenius J
    4. Patel K
    5. Ermentrout B
    6. Urban NN

    (2020) Mouse navigation strategies for odor source localization

    Frontiers in Neuroscience 14:218.

    https://doi.org/10.3389/fnins.2020.00218

    • PubMed
    • Google Scholar
48. 1. Lockery SR
    2. Lawton KJ
    3. Doll JC
    4. Faumont S
    5. Coulthard SM
    6. Thiele TR
    7. Chronis N
    8. McCormick KE
    9. Goodman MB
    10. Pruitt BL

    (2008) Artificial dirt: Microfluidic substrates for nematode neurobiology and behavior

    Journal of Neurophysiology 99:3136–3143.

    https://doi.org/10.1152/jn.91327.2007

    • Google Scholar
49. 1. Louis M
    2. Huber T
    3. Benton R
    4. Sakmar TP
    5. Vosshall LB

    (2008) Bilateral olfactory sensory input enhances chemotaxis behavior

    Nature Neuroscience 11:187–199.

    https://doi.org/10.1038/nn2031

    • PubMed
    • Google Scholar
50. 1. Luo L
    2. Wen Q
    3. Ren J
    4. Hendricks M
    5. Gershow M
    6. Qin Y
    7. Greenwood J
    8. Soucy ER
    9. Klein M
    10. Smith-Parker HK
    11. Calvo AC
    12. Colón-Ramos DA
    13. Samuel ADT
    14. Zhang Y

    (2014) Dynamic encoding of perception, memory, and movement in a C. elegans chemotaxis circuit

    Neuron 82:1115–1128.

    https://doi.org/10.1016/j.neuron.2014.05.010

    • Google Scholar
51. 1. Matheson AMM
    2. Lanz AJ
    3. Medina AM
    4. Licata AM
    5. Currier TA
    6. Syed MH
    7. Nagel KI

    (2022) A neural circuit for wind-guided olfactory navigation

    Nature Communications 13:4613.

    https://doi.org/10.1038/s41467-022-32247-7

    • PubMed
    • Google Scholar
52. 1. Mattingly HH
    2. Kamino K
    3. Machta BB
    4. Emonet T

    (2021) Escherichia coli chemotaxis is information limited

    Nature Physics 17:1426–1431.

    https://doi.org/10.1038/s41567-021-01380-3

    • PubMed
    • Google Scholar
53. 1. Monte P
    2. Woodard C
    3. Ayer R
    4. Lilly M
    5. Sun H
    6. Carlson J

    (1989) Characterization of the larval olfactory response in Drosophila and its genetic basis

    Behavior Genetics 19:267–283.

    https://doi.org/10.1007/BF01065910

    • PubMed
    • Google Scholar
54. 1. Park SK
    2. Shanbhag SR
    3. Dubin AE
    4. de Bruyne M
    5. Wang Q
    6. Yu P
    7. Shimoni N
    8. D’Mello S
    9. Carlson JR
    10. Harris GL
    11. Steinbrecht RA
    12. Pikielny CW

    (2002) Inactivation of olfactory sensilla of a single morphological type differentially affects the response of Drosophila to odors

    Journal of Neurobiology 51:248–260.

    https://doi.org/10.1002/neu.10057

    • PubMed
    • Google Scholar
55. 1. Pierce-Shimomura JT
    2. Morse TM
    3. Lockery SR

    (1999) The fundamental role of pirouettes in Caenorhabditis elegans chemotaxis

    The Journal of Neuroscience 19:9557–9569.

    https://doi.org/10.1523/JNEUROSCI.19-21-09557.1999

    • PubMed
    • Google Scholar
56. 1. Radvansky BA
    2. Dombeck DA

    (2018) An olfactory virtual reality system for mice

    Nature Communications 9:839.

    https://doi.org/10.1038/s41467-018-03262-4

    • PubMed
    • Google Scholar
57. 1. Risse B
    2. Thomas S
    3. Otto N
    4. Löpmeier T
    5. Valkov D
    6. Jiang X
    7. Klämbt C

    (2013) FIM, a novel FTIR-based imaging method for high throughput locomotion analysis

    PLOS ONE 8:e53963.

    https://doi.org/10.1371/journal.pone.0053963

    • PubMed
    • Google Scholar
58. 1. Schmuker M
    2. Bahr V
    3. Huerta R

    (2016) Exploiting plume structure to decode gas source distance using metal-oxide gas sensors

    Sensors and Actuators B 235:636–646.

    https://doi.org/10.1016/j.snb.2016.05.098

    • Google Scholar
59. 1. Tadres D
    2. Wong PH
    3. To T
    4. Moehlis J
    5. Louis M

    (2022) Depolarization block in olfactory sensory neurons expands the dimensionality of odor encoding

    Science Advances 8:eade7209.

    https://doi.org/10.1126/sciadv.ade7209

    • PubMed
    • Google Scholar
60. 1. Tanimoto Y
    2. Yamazoe-Umemoto A
    3. Fujita K
    4. Kawazoe Y
    5. Miyanishi Y
    6. Yamazaki SJ
    7. Fei X
    8. Busch KE
    9. Gengyo-Ando K
    10. Nakai J
    11. Iino Y
    12. Iwasaki Y
    13. Hashimoto K
    14. Kimura KD

    (2017) Calcium dynamics regulating the timing of decision-making in C. elegans

    eLife 6:e21629.

    https://doi.org/10.7554/eLife.21629

    • PubMed
    • Google Scholar
61. 1. Tariq MF
    2. Lewis SM
    3. Lowell A
    4. Moore S
    5. Miles JT
    6. Perkel DJ
    7. Gire DH

    (2021) Using head-mounted ethanol sensors to monitor olfactory information and determine behavioral changes associated with ethanol-plume contact during mouse odor-guided navigation

    eNeuro 8:ENEURO.0285-20.2020.

    https://doi.org/10.1523/ENEURO.0285-20.2020

    • PubMed
    • Google Scholar
62. 1. Tastekin I
    2. Khandelwal A
    3. Tadres D
    4. Fessner ND
    5. Truman JW
    6. Zlatic M
    7. Cardona A
    8. Louis M

    (2018) Sensorimotor pathway controlling stopping behavior during chemotaxis in the Drosophila melanogaster larva

    eLife 7:e38740.

    https://doi.org/10.7554/eLife.38740

    • PubMed
    • Google Scholar
63. 1. Torayama I
    2. Ishihara T
    3. Katsura I

    (2007) Caenorhabditis elegans integrates the signals of butanone and food to enhance chemotaxis to butanone

    The Journal of Neuroscience 27:741–750.

    https://doi.org/10.1523/JNEUROSCI.4312-06.2007

    • PubMed
    • Google Scholar
64. 1. Vergassola M
    2. Villermaux E
    3. Shraiman BI

    (2007) “Infotaxis” as a strategy for searching without gradients

    Nature 445:406–409.

    https://doi.org/10.1038/nature05464

    • PubMed
    • Google Scholar
65. 1. Vickers NJ
    2. Baker TC

    (1994) Reiterative responses to single strands of odor promote sustained upwind flight and odor source location by moths

    PNAS 91:5756–5760.

    https://doi.org/10.1073/pnas.91.13.5756

    • PubMed
    • Google Scholar
66. 1. Worthy SE
    2. Haynes L
    3. Chambers M
    4. Bethune D
    5. Kan E
    6. Chung K
    7. Ota R
    8. Taylor CJ
    9. Glater EE

    (2018) Identification of attractive odorants released by preferred bacterial food found in the natural habitats of C. elegans

    PLOS ONE 13:e0201158.

    https://doi.org/10.1371/journal.pone.0201158

    • PubMed
    • Google Scholar
67. 1. Yamada M
    2. Ohashi H
    3. Hosoda K
    4. Kurabayashi D
    5. Shigaki S

    (2021) Multisensory-motor integration in olfactory navigation of silkmoth, Bombyx mori, using virtual reality system

    eLife 10:e72001.

    https://doi.org/10.7554/eLife.72001

    • PubMed
    • Google Scholar
68. 1. Yamazoe-Umemoto A
    2. Fujita K
    3. Iino Y
    4. Iwasaki Y
    5. Kimura KD

    (2015) Modulation of different behavioral components by neuropeptide and dopamine signalings in non-associative odor learning of Caenorhabditis elegans

    Neuroscience Research 99:22–33.

    https://doi.org/10.1016/j.neures.2015.05.009

    • PubMed
    • Google Scholar
69. 1. Yamazoe-Umemoto A
    2. Iwasaki Y
    3. Kimura KD

    (2018) Measuring spatiotemporal dynamics of odor gradient for small animals by gas chromatography

    Bio-Protocol 8:e2797.

    https://doi.org/10.21769/BioProtoc.2797

    • PubMed
    • Google Scholar
70. 1. Yoshida K
    2. Hirotsu T
    3. Tagawa T
    4. Oda S
    5. Wakabayashi T
    6. Iino Y
    7. Ishihara T

    (2012) Odour concentration-dependent olfactory preference change in C. elegans

    Nature Communications 3:739.

    https://doi.org/10.1038/ncomms1750

    • PubMed
    • Google Scholar

Author details

1. Kevin S Chen

   Princeton Neuroscience Institute, Princeton University, Princeton, United States

   Contribution

   Software, Formal analysis, Investigation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Rui Wu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8792-4625

2. Rui Wu

   Department of Physics, New York University, New York, United States

   Contribution

   Resources, Software, Validation, Methodology, Writing – review and editing

   Contributed equally with

   Kevin S Chen

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0009-0008-8707-4075

3. Marc H Gershow

   1. Department of Physics, New York University, New York, United States
   2. Center for Neural Science, New York University, New York, United States

   Contribution

   Conceptualization, Resources, Formal analysis, Supervision, Funding acquisition, Visualization, Methodology, Project administration, Writing – review and editing

   For correspondence

   mhg4@nyu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7528-6101

4. Andrew M Leifer

   1. Princeton Neuroscience Institute, Princeton University, Princeton, United States
   2. Department of Physics, Princeton University, Princeton, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   leifer@princeton.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5362-5093

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