Abstract
Odours are transported in turbulent plumes resulting locally in highly fluctuating odour concentration (Celani et al., 2014; Murlis et al., 1992; Mylne and Mason, 1991; Shraiman and Siggia, 2000). Yet, whether mammals can make use of the ensuing temporal structure (Celani et al., 2014; Crimaldi and Koseff, 2001; Murlis et al., 1992; Mylne and Mason, 1991; Schmuker et al., 2016; Vickers, 2000) to extract information about the olfactory environment remains unknown. Here, we use dual-energy photoionisation recording with >300 Hz bandwidth to simultaneously determine odour concentrations of two odours in air. We show that temporal correlation of odour concentrations reliably predicts whether odorants emerge from the same or different sources in normal turbulent environments outside and in laboratory conditions. To replicate natural odour dynamics in a reproducible manner we developed a multichannel odour delivery device allowing presentation of several odours with 10 ms temporal resolution. Integrating this device in an automated operant conditioning system we demonstrate that mice can reliably discriminate the correlation structure of odours at frequencies of up to 40 Hz. Consistent with this finding, output neurons in the olfactory bulb show segregated responses depending on the correlation of odour stimuli with populations of 10s of neurons sufficient to reach behavioural performance. Our work thus demonstrates that mammals can perceive temporal structure in odour stimuli at surprisingly fast timescales. This in turn might be useful for key behavioural challenges (Jacobs, 2012) such as odour source separation (Hopfield, 1991), figure-ground separation (Rokni et al., 2014) or odour localisation (Vergassola et al., 2007; Vickers, 2000).
The turbulent nature of air as well as water flow results in complex temporal fluctuations of odour concentrations that depend on the distance and direction of odour sources (Atema, 1995; Baker et al., 1998; Balkovsky and Shraiman, 2002; Celani et al., 2014; Crimaldi and Koseff, 2001; Fackrell and Robins, 1982; Justus et al., 2002; Mafraneto and Carde, 1994; Moore and Atema, 1991; Murlis et al., 1992; Murlis et al., 2000; Mylne and Mason, 1991; Schmuker et al., 2016; Vergassola et al., 2007; Vickers, 2000; Vickers et al., 2001; Weissburg et al., 2012; Weissburg et al., 2002). Natural odours consist of multiple different types of molecules (Mori and Yoshihara, 1995), and a typical olfactory scene contains several of such sources (Rokni et al., 2014), with possibly overlapping chemical compositions. Thus, in order to identify naturally complex odours, the first challenge the olfactory system faces is to separate odour sources and possibly attribute different chemicals to different sources (Hopfield, 1991; Jacobs, 2012). Motivated by the turbulent nature of odour transport, it was suggested that the temporal structure of odour concentration fluctuations might contain information regarding odour source (Hopfield, 1991) - i.e. that chemicals belonging to the same sources would co-fluctuate in concentration, whilst chemicals belonging to different sources would be largely uncorrelated. Therefore if animals can detect these correlation structures they would easily be able to perceive which odours arise from the same object.
In order to test this in air, we developed a dual-energy fast photoionisation detection (defPID) approach to measure odour concentrations of two odours simultaneously with high temporal bandwidth (methods, Fig 1a-c, Supp Figs 1.1–1.2). When an odour was presented in a laboratory environment with artificially generated turbulence (Fig 1a), odour concentration was indeed highly fluctuating with a spectrum extending beyond 40 Hz (Supp Fig 1.1f). If two odours were presented from the same source, these fluctuations were highly correlated (Fig 1a,b). When we separated odour sources and presented the two odours from as little as 50 cm apart, however, odour dynamics were almost completely uncorrelated (Fig 1a,b) with intermediate correlations for closer distances (Fig 1b). This pattern of almost perfect correlation for same source and virtually uncorrelated dynamics for sources separated by as little as 50 cm was maintained at closer and further distances between odour source and sensor (Supp Fig 1.2a,b), independent of the odours used (Supp Fig 1.2c) and mirrored in a natural environment (outside, Fig 1c). Thus, the correlation structure of odorant concentration fluctuations indeed contains reliable information about the distribution of odour sources in air. Can mice make use of this information? In order to reproducibly probe the behavioural response to fluctuating stimuli we constructed a high bandwidth odour delivery device based on high-speed solenoid valves operating at >1 kHz (methods, Fig 1d-h, Supp Fig 1.3–1.4). Odour pulses could be generated at frequencies of 40 Hz with minimal loss in fidelity (Fig 1e,f) for different odours (Supp Fig 1.3–1.4). Pulse-width modulation at sub-millisecond time scales makes rapid and reproducible concentration adjustment possible (Fig 1.3c) allowing us to reliably emulate the dynamic features of natural plumes. A multiple channel version of the odour delivery device in turn could reliably produce odour pulses with different temporal correlation structures (Fig 1h).
In order to assess whether mice could discriminate between such stimuli at different frequencies, we developed an automatic operant conditioning system (“AutonoMouse”, Fig 2a, Supp Video 1 (Erskine et al., 2019)), where cohorts of up to 25 mice could be trained in operant conditioning tasks simultaneously. Mice in the system were group-housed and RFID-tagged for individual identification, remaining in the apparatus for periods of up to 18 months. Food was freely accessible but water could be obtained only by completion of go/no-go operant conditioning tasks. Mice indeed readily learned to differentially respond to correlated or anti-correlated odours (Fig 2b-d). Gradually increasing the correlation frequency showed that animals were capable of reliably detecting the correlation structure of the stimuli at frequencies of up to 40 Hz (Fig 2d-f). As a population, animal performance decreased by approximately 5 % per octave with performance significantly above chance at frequencies of up to 40 Hz (n=33 mice in two cohorts of 14 and 19 mice, Fig 2f, Supp Fig 2.3). To mitigate the risk of animals using non-intended cues for discrimination, odours were presented from changing combinations of valves for each trial (Fig 2c, Supp Fig 2.1), odour flow was carefully calibrated and additionally varied randomly between trials such that neither flow nor average concentration provided any information about the nature of the stimulus (Supp Fig 2.1e,f). Consistent with this, when valve identities were scrambled, animals performed at chance (orange, Fig 2f, Supp Fig 2.3). Finally, when odour presentation was changed to a new set of valves (Abraham et al., 2004), performance levels were maintained (Supp Fig 2.1d, inset Fig 2d), indicating that only intended cues (the temporal structure of odours) were used for discrimination.
Consistent with the fact that mice can detect temporally fluctuating odours, we find neurons that differentially respond to correlated and anti-correlated odours already in the output of the olfactory bulb using 2p imaging (Fig 3a-d, Supp Fig 3.1). Using linear classifier analysis (Cury and Uchida, 2010), we find that 10s of randomly chosen mitral/tufted cells are sufficient to guarantee the distinction of 20 Hz correlated and anti-correlated stimuli on a trial by trial basis (Fig 3d,e).
Here, we have shown that mammals can detect temporal features of odour stimuli at frequencies of up to 40 Hz. This surprisingly high frequency is consistent with recent findings that the olfactory bulb circuitry not only enables highly precise odour responses (Cury and Uchida, 2010; Shusterman et al., 2011) but also enables detection of optogenetically evoked inputs with a precision of 10-30 ms (Rebello et al., 2014; Smear et al., 2011). While behavioural and physiological responses to precisely timed odour stimuli have been observed in insects (Brown et al., 2005; Geffen et al., 2009; Nagel et al., 2015; Riffell et al., 2014; Szyszka et al., 2014; Vickers et al., 2001), in mammals the complex structure of the nasal cavity was generally thought to “wash-out” any temporal structure of the incoming odour plume ((Kepecs et al., 2006) see however (Gupta et al., 2015)). Our results show that on the contrary mice can readily make use of information in odour stimuli fluctuating at frequencies of up to 40 Hz.
What could such high bandwidth be useful for? We have shown that odour sources even in close proximity differ in the temporal correlation structure. Thus, the behavioural ability to detect whether odorants are temporally correlated could allow mice to perform source separation segmentation, solving the “olfactory cocktail party problem” (Hopfield, 1991; Rokni et al., 2014) purely using temporal structure, without requiring the presence of unique chemicals in individual sources or prior knowledge of odours or components of odours (Grabska-Barwinska et al., 2017). Furthermore, extracting temporal features from odour fluctuations could allow for behavioural detection of distance or direction of an odour source (Celani et al., 2014; Justus et al., 2002; Mafraneto and Carde, 1994; Moore and Atema, 1988; Schmuker et al., 2016).
How could this temporal information be extracted? While insects are able to detect the simultaneity of onset of two odours (Baker et al., 1998; Stierle et al., 2013; Szyszka et al., 2012), we find that it is unlikely that this is the sole or even dominant means that mice use to detect correlation (Supp Fig 2.4). Similarly, mice do not show adjustment of sniff strategies in the behavioural experiment (Supp Fig 2.5, Supp Video 2-3). Already OSNs might be tuned to specific temporal features as shown in crustaceans (Park et al., 2014). While individual mammalian OSNs are thought to be quite slow and unreliable (Duchamp-Viret et al., 1999), the large convergence of OSN axons can provide a substrate to create the needed high temporal bandwidth (Abeles, 2004). Either intrinsic cellular biophysics (Hausser et al., 2000), local interneurons or long-range lateral inhibition (Fukunaga et al., 2014; Hopfield, 1991) might in turn permit the extraction of temporal correlation within the olfactory bulb circuitry.
The turbulence of odour plumes has often been viewed as a source of noise for mammals. Our finding that mice can extract temporal information from odour stimuli at a bandwidth of up to 40 Hz opens up a new perspective on how mice could make use of natural turbulence in order to obtain information about their spatial environment, providing new challenges to uncover what algorithms and computations the mammalian olfactory system might implement.
Methods
Animals
All animal procedures performed in this study were approved by the UK government (Home Office) and by Institutional Animal Welfare Ethical Review Panel. All mice used for behavioural experiments were C57/Bl6 males.
Reagents
All odours were obtained in their pure form from Sigma-Aldrich, St. Louis MO, USA. Unless otherwise specified, odours were diluted 1/5 with mineral oil in 15 ml glass vials (27160-U, Sigma-Aldrich, St. Louis MO, USA).
High-speed odour delivery device
The odour delivery device was based on a modular design of four separate odour channels, and consisted of an odour manifold for odour storage, a valve manifold for control of odour release and hardware for controlling and directing air flow through the system. The odour manifold was a 12.2×3.2×1.5 cm stainless steel block with 4 milled circular indentations (1 cm radius). Within each of these indentations was a threaded through-hole for installation of an input flow controller (AS1211F-M5-04, SMC, Tokyo, Japan) and an output filter (INMX0350000A, The Lee Company, Westbrook CT, USA). For each inset, the cap of a 15 ml glass vial (27160-U, Sigma-Aldrich, St. Louis MO, USA) with the centre removed was pushed in and sealed with epoxy resin (Araldite Rapid, Huntsman Advanced Materials, Basel, Switzerland). This meant that glass vials could be screwed in and out of the insets.
Solenoid valves typically limit high-fidelity odour stimulation resulting in odour rise times of several 10s of milliseconds under optimal conditions (Raiser et al., 2017). We thus employed highspeed micro-dispense valves with custom electronics for pulse-width modulation to maximize bandwidth: 4 VHS valves (INKX0514750A, The Lee Company, Westbrook CT, USA) were installed in a 4-position manifold (INMA0601340B, The Lee Company, Westbrook CT, USA) with standard mounting ports (IKTX0322170A, The Lee Company, Westbrook CT, USA). Each valve was connected to a corresponding odour position in the odour manifold with 10 cm Teflon tubing (TUTC3216905L, The Lee Company, Westbrook CT, USA). Each valve was controlled by digital commands via a spike-and-hold driver. Each digital pulse delivered to the spike-and-hold driver delivered a 0.5 ms, 24 V pulse to the valve (to open it), followed by a 3.3 V holding pulse lasting the rest of the duration of the digital pulse. This spike-and-hold input allowed for fast cycling of the valve without switching between 0 and 24 V at high frequencies to prevent from overheating the valve. Each valve was controlled by an individual spike-and-hold driver. Up to 4 drivers could be controlled and powered with a custom-made PSU consisting of a 24 V power input and a linear regulator to split the voltages into a 24 V and 3.3 V line, as well as control inputs taking digital signal input and routing it to the appropriate valve. Pulse profiles for calibration and stimulus production were generated with custom Python software (PyPulse, PulseBoy; github.com/RoboDoig) allowing to define pulse parameters across multiple valves using a GUI.
To generate air flow through the olfactometer, a pressurised air source was connected to a filter (AME250C-F02, SMC, Tokyo, Japan) and demister (AMF250CF02, SMC, Tokyo, Japan) and then split into two separate lines, the input line and carrier line. Both lines were then connected to a pressure regulator (AR20-F01BG-8, SMC, Tokyo, Japan) and flow controller (FR2A13BVBN, Brooks Instrument, Hatfield PA, USA). The main line was then connected to the input of the valve manifold. The input line was split into 4 separate lines and connected to the input flow controllers (set to 0.25 L/min) on each odour position of the odour manifold. The output of the valve manifold was fitted with MINSTAC tubing (TUTC3216905L, The Lee Company, Westbrook CT, USA). Where the design was scaled up (e.g. to include 8 odour positions) the valve manifold outputs were connected and consolidated to a single output with 3-way connectors (QSMY-6-4, Festo, Esslingen am Neckar, Germany). Shape and reliability of odour pulses depended strongly on low volume headspace and low pressure levels (0.05 MPa).
Odour characterisation
Signal fidelities were calculated by first subtracting the average amplitude of troughs from the average amplitude of peaks during a pulse train and then subsequently dividing this peak-to-trough value by the difference of average peak amplitude subtracted by baseline amplitude. This results in a value between 0 and 1, with 1 being perfectly modulated odour pulses.
Behaviour
Automatic operant conditioning of cohorts of mice (AutonoMouse)
In AutonoMouse, groups of mice (up to 25) implanted with an RFID chip are housed in a common home cage (Fig 2a, for detailed description see Erskine et al., 2019). Within the common home cage of AutonoMouse, mice have free access to food, social interaction and environmental enrichment. Water is not freely available in the system, but can be gained at any time by completion of an operant conditioning go/no-go task. To access these behavioural tasks, mice must leave the home cage and enter a behavioural area. This behavioural area contains the odour port and a lick port through which water rewards can be released. The lick port is also connected to a lick sensor which registers the animal’s response (its lick rate) in response to the task stimuli. As animals can only gain their daily water intake by completing behavioural tasks, mice are motivated to complete long sequences of trials without manual water restriction.
Training on temporally structured odours
We aimed to probe whether mice could perceive a particular temporal feature of naturally occurring odour signals: temporal correlations between odour signals. In particular we aimed to investigate this question with the simplest possible case: whether mice could discriminate perfectly correlated from perfectly anti-correlated odour stimuli.
All tasks followed a standard go/no-go training paradigm. Animals were presented with two odours presented in either a correlated pattern or an anti-correlated pattern (Fig 2b, Supp Fig 2.1a-c). For roughly half of all animals, the correlated pattern was S+ (rewarded) and the anti-correlated pattern was S-(unrewarded); in the other half of the group this reward valence was reversed. All stimuli were 2 s long. A water reward could be gained by licking such that licking was detected for at least 10 % of the stimulus time during an S+ presentation. Licking for the same amount of time during S-presentation resulted in a timeout interval of 7 s. In all other response cases the inter-trial interval was 3 s and no water reward was delivered.
Stimulus structure
All anti-correlated and correlated stimuli on each trial followed a common pattern in their construction. Generally, wherever an odour position is inactivated a blank position should be activated to compensate for flow change. There should also be no consistent differences in the amount of odour or flow released during the stimulus between correlated and anti-correlated stimuli. The detailed algorithm for stimulus generation is as follows:
Choose whether the stimulus will be correlated or anti-correlated.
A set of 1-2 positions each for odour 1 and odour 2 and 2-3 positions for blank are randomly chosen from a pre-defined subset of 6 of the 8 total positions. For example, a valid combination could be odour 1 at position 1, 2; odour 2 at position 5; and blank at position 3 and 7. (see Fig 2c, Supp Fig 2.1b)
Create a guide pulse at the desired frequency (e.g. 2 Hz pulse with 50% duty) for all positions that follows the chosen stimulus structure.
The relative contributions of each position to the total stimulus are randomly generated. At each time point in the stimulus, only two position types should be active (e.g. odour 1 and blank for an anti-correlated stimulus) so the maximum contribution for any position type is 50% of the total release amount. Where two positions have been chosen for a position type, their relative contributions should add to 50%.
The guide pulses are pulse-width modulated according to the relative contributions of each position. PWM is at 500 Hz with some added jitter in the duty to avoid strong tone generation.
Task structure
Task frequency was randomised from trial to trial in a range between 2-81 Hz. The choice of frequency was with weighted probability divided into 3 frequency bands. E.g. this task could be arranged such that 2-20 Hz would be chosen with P = 0.6, 21-40 Hz with P = 0.3 and 41-81 Hz with P = 0.1. Within each of these frequency bands, the choice of individual task frequency was based on a uniform distribution.
Onset detection
For the onset detection experiments (Supp Fig 2.4) animals were trained to discriminate perfectly correlated (e.g. S+) from perfectly anti-correlated stimuli (e.g. S-) and probed with partially altered stimuli where the onset (first cycle) of the probe S+ stimuli was anti-correlated and probe S-stimuli where the onset (first cycle) was correlated. Performance during these probe trials is then compared to the average performance during training (perftrain).
We calculated the expected average animal performance on the probe trials based on two models: Model 1 assumed the animals were taking any part of the stimulus into account equally when making a decision. Model 2 assumed that only the onset of the stimulus would contribute to discrimination. For Model 1 thus, a stimulus of frequency f (e.g. 10 Hz) that was sampled for tsampleconsisted of a “shifted” onset component of one cycle for S+ (1/f) and half a cycle for S-(0.5/f) corresponding to a fraction of fraconset= 1/f/tsampleof the entire stimulus and a “normal” residual (fracres= 1-fraconset). Thus the predicted probe trial performance would be: For Model 2, ignoring inhalation timing, the prediction would be that preference would be reversed (as onset correlations during probe trials are reversed). However, this ignores the fact that odour stimuli during the exhalation period might not be detected. Thus, to more accurately predict animals’ performance for Model 2, we assume that the part of the stimulus that is detected as the “onset” is the first odour pulse during an inhalation phase. During the probe trial, this will be the “inverted” first cycle if the stimulus begins either during the inhalation phase or at most 1/f before the inhalation (then inhalation would start during the inverted first cycle of the probe trial). The probability of this occurring is perfonset= (durinh+1/f) / dursinffwith durinhand dursinffbeing inhalation and sniff duration respectively (provided durinh+1/f < dursinff). Predicted probe trial performance for an “onset only” model would thus be:
Controls
Control valves could be automatically added to the random frequency task. These tasks produced their stimuli based on a subset of 6 valves, control valves could be added automatically after a set period of trials to force the algorithm to produce stimuli from all 8 valves (see Fig 2c, switch control).
In switch controls, the experiment was halted and the positions of all valves and odour positions were shuffled in the olfactometer (see Fig 2c, switch control). The software definitions for valve positions were remapped to account for the change and the experiment was restarted.
A subgroup of animals was created in which the valve map was scrambled, as an ongoing control against animals learning extraneous variables in the task (see Fig 2c, scramble control). The valve map was scrambled in the following way: One blank to odour 1, one odour 2 to blank, one odour 1 to odour 2 and one odour 1 to blank.
Cohorts
The correlation discrimination experiment was performed in 2 separate experimental cohorts (group 1, n = 14; group 2, n = 25). Each cohort was organised into several subgroups which performed slight variations of the behavioural tasks in terms of reward valence and valves utilised, but with the same underlying task aim. Half of the animals in each subgroup were trained on correlated stimuli as the S+ rewarded condition, with the other half trained on anti-correlated as rewarded. Animals were further subdivided into groups which were trained on different subsets of valves as standard in the 8-channel olfactometer. For each cohort, mice were once assigned to each of these subgroups based on performance in a simple pure odour discrimination at the beginning of the experiment - group membership was randomised until no significant (ANOVA, Tukey-Kramer) differences in performance could be extracted between these subgroups on this task.
Data Analysis
AutonoMouse behavioural data was converted to MATLAB data format using the Conversion module of the Python autonomouse-control package (github.com/RoboDoig). All subsequent analysis was performed with custom-written MATLAB scripts unless otherwise specified.
All behavioural performance within a specified trial bin was calculated as a weighted average of S+ vs. S-performance: Where S+ is the total number of rewarded trials, S-is the total number of unrewarded trials, Hit is the total number of rewarded trials in which a lick response was detected, CR (correct rejection) is the total number of unrewarded trials in which no lick response was detected.
For random stimulus pulse frequency experiments (e.g. Fig. 2e,f) trials were binned approximately by half-octave for performance analysis. The exact intervals were Hz = [2, 3, 4, 5, 6:7, 8:10, 11:13, 14:17, 18:22, 23:29, 30:37, 38:48, 49:62, 63:81]. Reaction time (Supp Fig. 2.2) was calculated from S+ trials for each animal as the time to the first lick after stimulus onset.
Motion magnification of the respiration camera video recordings was performed with phase-based video motion processing with correction for large body movements based on MATLAB scripts by Wadhwa et al., 2013 (phaseAmplifyLargeMotions). Parameters for phase amplification were: blurring σ = 1, magnification α = 50, amplification in frequency band between 2-13 Hz. Following magnification, static ROIs for each video were selected in Bonsai (http://www.kampff-lab.org/bonsai/, Lopes et al., 2015) over the animal flank. An adaptive binary threshold was applied to the ROI to segment the animal body from the video background. Respiration rate was extracted as the total size of the ROI occupied by the body over time.
In vivo two-photon imaging
Surgical and experimental procedures
Prior to surgery all utilised surfaces and apparatus were sterilised with 1% trigene. 12-20 week old Tbet-cre mice (Haddad et al., 2013) crossed with a GCaMP6f reporter line (Otazu et al., 2015) were anaesthetised using a mixture of fentanyl/midazolam/medetomidine (0.05 mg/kg, mg/kg, 0.5 mg/kg respectively). Depth of anaesthesia was monitored throughout the procedure by testing the toe-pinch reflex. The fur over the skull and at the base of the neck was shaved away and the skin cleaned with 1 % chlorhexidine scrub. Mice were then placed on a thermoregulator (DC Temperature Controller, FHC, ME USA) heat pad controlled by a temperature probe inserted rectally. While on the heat pad, the head of the animal was held in place with a set of ear bars. The scalp was incised and pulled away from the skull with four arterial clamps at each corner of the incision. A custom head-fixation implant was attached to the base of the skull with medical super glue (Vetbond, 3M, Maplewood MN, USA) such that its most anterior point rested approximately 0.5 mm posterior to the bregma line. Dental cement (Paladur, Heraeus Kulzer GmbH, Hanau, Germany; Simplex Rapid Liquid, Associated Dental Products Ltd., Swindon, UK) was then applied around the edges of the implant to ensure firm adhesion to the skull. A craniotomy over the left olfactory bulb (approximately 2 x 2 mm) was made with a dental drill (Success 40, Osada, Tokyo, Japan) and then immersed in ACSF (NaCl (125 mM), KCl (5 mM), HEPES (10 mM), pH adjusted to 7.4 with NaOH, MgSO4.7H2O (2 mM), CaCl2.2H2O (2 mM), glucose (10 mM)) before removing the skull with forceps. The dura was then peeled back using fine forceps. A layer of 2 % low-melt agarose diluted in ACSF was applied over the exposed brain surface before placing a glass window cut from a cover slip (borosilicate glass 1.0 thickness) using a diamond knife (Sigma-Aldrich) over the craniotomy. The edges of the window were then glued with medical super glue (Vetbond, 3M, Maplewood MN, USA) to the skull.
Following surgery, mice were placed in a custom head-fixation apparatus and transferred to a two-photon microscope rig along with the heat pad. The microscope (Scientifica Multiphoton VivoScope) was coupled with a MaiTai DeepSee laser (Spectra Physics, Santa Clara, CA) tuned to 940 nm (<50 mW average power on the sample) for imaging. Images (512 x 512 pixels) were acquired with a resonant scanner at a frame rate of 30 Hz using a 16x 0.8 NA water-immersion objective (Nikon). The output of a 4-channel version of the temporal olfactometer described above was adjusted to approximately 1 cm away from the ipsilateral nostril to the imaging window, and a flow sensor was placed to the contralateral nostril for continuous respiration recording.
Awake recordings
For implantation of the head-plate, mice were anaesthetized with isoflurane in 95% oxygen (5 % for induction, 1.5 % - 3 % for maintenance). Local (mepivacaine, 0.5 % s.c.) and general analgesics (carprofen 5 mg/kg s.c.) were applied immediately at the onset of surgery. After surgery, animals were allowed to recover for 7 days with access to wet diet and, after recovery, habituated to the head-fixed situation for at least 15 min on three consecutive days preceding the imaging experiment (Supp Fig 3.1).
Odour stimulation
Stimuli were generated from mixtures of physically mixed monomolecular odorants in order to ensure high probability of finding odour responsive cells in the dorsal olfactory bulb using custom Python Software (PulseBoy). Binary mixtures were diluted in mineral oil at the ratio of 1:5 and installed into the 4-channel olfactometer (15 ml per vial) along with two blank positions (15 ml mineral oil). Mix A: ethyl butyrate + 2-hexanone, mix B: isopentyl acetate + cineole. For all stimuli, odour valve offsets were compensated by opening a corresponding blank position valve to ensure no global flow changes occurred over the course of the stimulus. All stimuli were repeated 24 times with a 20 s inter-stimulus interval.
Analysis
Initial analysis was performed with custom scripts in Fiji. From an average of ∼8000 frames, ROIs around cell somata were manually selected and calcium transients were extracted and exported for further analysis in MATLAB. All traces were aligned to the first inhalation after odour onset. Calcium response integrals were calculated in a 5 s window from odour onset. To analyse how well odour responses predicted stimulus correlation on a trial-to-trial basis, we generated a linear discriminant classifier from the data set and analysed prediction accuracy. For the classifier, we performed 50% holdout validation, splitting the data randomly into a training set and test set with equal numbers of samples. We then performed linear discriminant analysis on the training data set to determine the best linear boundary between correlated vs. anti-correlated data. Classifier performance was then validated on the test data set. To determine the effect of number of ROIs used on classifier performance, we iteratively trained multiple classifiers on random subsets of ROIs with increasing numbers of ROIs within each set. For each ROI subset size, 100 classifiers were trained and the mean +/−SEM of their performance accuracy was calculated.
Dual-energy fast photoionisation detection (defPID)
Two photoionisation detectors (200B miniPID, Aurora Scientific, Aurora ON, Canada) fitted with UV lamps of emission energy 10.6 eV (PID high) and 8.4 eV (PID low) were used to discriminate ethyl butyrate (EB, ionisation energy = 9.9 eV) from α-Terpinene (AT, ionisation energy = 7.6 eV) or ethyl valerate (EV, ionisation energy = 10.0 eV) from tripropyl amine (TA, ionisation energy = 7.2 eV). The PID inlets were connected with a 3-way connector to detect incoming odours by both PIDs simultaneously from a common point. PID heads were held on lab stands with the PID inlet at approximately 4 cm above ground level.
Odour delivery
Odours were held in ceramic crucibles (5 cm diameter, 6 ml volume) covered in an air-tight fashion using glass lids. Odours were released for 5 s with an inter-trial interval of 15 s by arduino-based robots programmed to lift the lids from the crucibles using a servo motor (TowerPro SG-5010, Adafruit, UK). PID recordings and robot movements were remotely controlled and synchronized from a computer. Experiments were carried out in a large open space, both indoors and outdoors.
Outdoors setup: PIDs and odour delivery system as described above were used to record for multiple trials in different conditions on a day with low wind (∼8-12 mph, based on BBC weather).
Indoors setup: A digitally controlled fan (2214F/2TDH0, ebm-papst, Chelmsford, UK) was placed at a distance of 440 cm facing the PID inlet. An exhaust line was situated behind the PID inlet to ensure the direction of air from the fan towards the PID inlet. During a recording, the fan was set to maximum speed such that it pushed approximately 552 cf/min (cubic feet per minute) of air towards the PID inlet. A 25×25×25 cm Thermocool box was placed 200 cm downwind of the fan acting as an obstacle to air movement, breaking up any laminar air flow and ensuring turbulent air movement at the PID inlet.
Recording conditions
6 ml of the desired odour(s) were filled in two crucibles and placed in different locations based on the experimental conditions as described below:
Low energy only: The low-energy odour (AT or TA) was placed 40 cm (radial distance) away from the PID inlet, and displaced either 25 cm left or 25 cm right of the midline (the midline in this context is the line between the PID inlet and the centre of the fan). The odour source was alternated between left and right positioning relative to the midline an equal number of times to remove any possible bias from positioning in the air stream. The purpose of this recording condition was to generate data to calculate the linear transformation from the low energy signal to the high energy signal (Supp Fig1.1c,d).
Mix: 3 ml EB + 3 ml AT (or 3 ml EV + 3 ml TA) was pipetted in one crucible and placed either 25 cm left or 25 cm right of the midline at radial distances of 20 cm, 40 cm and 60 cm. The purpose of this recording condition was to determine how the temporal structure of individual odours in a plume behaved when the odours emitted from the same source.
Separate: 3 ml EB and 3 ml of AT (or 3 ml EV + 3 ml TA) were individually pipetted in two different crucibles and placed at a radial distance of 40 cm from the PID inlet. For the 50 cm apart condition, one odour source was placed 25 cm left of the midline while the other 25 cm on the right of the midline and vice-versa (equal number of trials for both cases) separating the odour sources by 50 cm. This procedure was repeated for lateral distances of 30 cm and 10 cm. The ‘50 cm apart’ case was repeated for radial distances of 20 cm and 60 cm. The purpose of this recording condition was to determine how the temporal structure of individual odours in a plume behaved when the odours emitted from separated sources but were still free to mix in air.
Analysis
Decomposition procedure: The low energy odour (AT) was recorded using both PIDs. Assuming a linear relation between the recorded signals from the 2 PIDs, we plotted the recorded events with a linear regression fit (Supp Fig 1.1c) and calculated slope and R2 value of the fit. The scaling factor (6.82 +/−SD 0.356) was calculated as the average slope of all linear fits for R2 ≥ 0.9.
The PID low traces were multiplied by this scaling factor which was termed as the estimated low energy odour (Supp Fig 1.1e). The estimated high energy odour was calculated by subtracting the estimated low energy odour from the PID high traces.
Correlation calculation: Custom written scripts in MATLAB (Mathworks, USA) were used to calculate the correlation coefficient between the estimated low energy odour and the estimated high energy odour for all conditions. Box plots were obtained from these values using Igor Pro 6 (WaveMetrics, USA).
Acknowledgements
We thank the animal facilities at National Institute for Medical Research and the Francis Crick Institute for animal care and technical assistance. We thank the mechanical and electronic workshops in Heidelberg (N. Neef, K. Schmidt, M. Lukat, R. Roedel, C. Kieser) and London (A. Ling, A. Hurst, M. Stopps) for excellent support during development and construction, the Aurora Scientific team for helpful suggestions for adapting the miniPID, T. Margrie for discussion and R. Jordan, C. Marin, J. Harris, F. Iacaruso, J. Kohl, and A. Fleischmann for comments on earlier versions of the manuscript. This work was supported by the Francis Crick Institute which receives its core funding from Cancer Research UK (FC001153), the UK Medical Research Council (FC001153), and the Wellcome Trust (FC001153); by the UK Medical Research Council (grant reference MC_UP_1202/5); a Wellcome Trust Investigator grant to AS (110174/Z/15/Z), and a DFG postdoctoral fellowship to TA.