Temporal responses of C. elegans chemosensory neurons are preserved in behavioral dynamics

Abstract

Animals track fluctuating stimuli over multiple timescales during natural olfactory behaviors. Here, we define mechanisms underlying these computations in Caenorhabditis elegans. By characterizing neuronal calcium responses to rapidly fluctuating odor sequences, we show that sensory neurons reliably track stimulus fluctuations relevant to behavior. AWC olfactory neurons respond to multiple odors with subsecond precision required for chemotaxis, whereas ASH nociceptive neurons integrate noxious cues over several seconds to reach a threshold for avoidance behavior. Each neuron's response to fluctuating stimuli is largely linear and can be described by a biphasic temporal filter and dynamical model. A calcium channel mutation alters temporal filtering and avoidance behaviors initiated by ASH on similar timescales. A sensory G-alpha protein mutation affects temporal filtering in AWC and alters steering behavior in a way that supports an active sensing model for chemotaxis. Thus, temporal features of sensory neurons can be propagated across circuits to specify behavioral dynamics.

Figure 1. AWC and ASH Respond Reliably to Simple and Complex Odor Patterns

Representative calcium transients (black) of AWC or ASH neurons expressing GCaMP3; stimulus input sequences are shown in red. For methods see Figure S1. (A,B) Step responses. (A) AWC response to odor removal after a five minute pre-exposure to 9.2×10⁻⁴ M isoamyl alcohol. (B) ASH response to addition of 1 M glycerol. (C,D) Flicker responses. (C) AWC response to a flickering on/off 9.2×10⁻⁴ M isoamyl alcohol stimulus with a 1 s pulse length, following five minute pre-exposure to isoamyl alcohol. (D) ASH response to a flickering on/off 1 M glycerol stimulus with a 1 s pulse length. Insets, magnified views of 10 s intervals after the response reached steady-state, stimulus in red. Gray vertical lines divide the inset graphs into 2 s epochs aligned to stimulus transitions. (E-H) Responses to pseudo-random m-sequence stimuli. (E) AWC response to a pseudo-random on/off 9.2×10⁻⁴ M isoamyl alcohol stimulus following a five-minute pre-exposure to isoamyl alcohol. (F) ASH response to a pseudo-random off/on pattern of 1 M glycerol. (E) and (F) used two repetitions of an m-sequence of pulse length 200 ms, 9-bit sequence. Brackets mark the second repetition, shown for additional neurons in (G) and (H), and used to construct and analyze the L-N model. The m-sequence stimulus input patterns were similar but with inverted sign; note the similar overall shape of the AWC and ASH neuronal responses but sharper temporal resolution in AWC traces. (G,H) Responses of additional AWC (G) or ASH (H) neurons to the same m-sequence as in (E) and (F). Brackets indicate two trials of a single animal, separated by ∼10 minutes. Individual traces were normalized to the peak magnitude within each trace.

Figure 2. AWC and ASH Linear Filters are Invariant and Robust

(A) Linear filters inferred from individual input-output records for AWC and ASH neurons (gray) and from trial-averaged input-output records (black). (B) AWC and ASH linear filters inferred from different m-sequence stimuli (sequence 2) compared to reference sequence from (A). Note the slight difference in ASH filters at late time points; an average of the two filters would likely reduce finite-length trial effects and provide a more accurate estimate of the “true” neural filter. (C) AWC linear filters derived without odor pre-exposure, using a GCaMP5 indicator, and in response to different isoamyl alcohol concentrations. For concentration analysis of ASH filters, see Figure S3. (D) Diagram of ASE or AWC anatomy, showing example regions of interest (boxes) for calcium imaging from the soma and axon. (E) Comparison of linear filters from somatic (red) versus axonal (black) calcium signals in AWC and ASH, with early timepoints magnified at right.

Figure 3. A Three-Variable ODE Model Produces a Biphasic Filter with Two Timescales

(A) Schematic of the overall linear-nonlinear model and putative mapping to elements of the signaling-to-GCaMP transformation. L_measured (L_meas) is a convolution of the intrinsic neuronal filter L_neuron and the GCaMP filter L_gCaMP. The nonlinearity N_measured (N_meas) probably arises predominantly from the GCaMP nonlinearity N_gCaMP. (B) Diagram and equations of a three-variable ODE model that produces a biphasic filter with distinct timescales for each phase, corresponding to the L_neuron filter in (A). See also Table S1. (C) Overall ODE model filter (in black) fit to the filter extracted from trial-averaged input-output records (in gray) for AWC and ASH. Technical issues limited the maximum lag of the estimated filters to 24 s, but the ODE model filters are extrapolated to 48 s. (D) Decomposition of ODE model filter into fast (A-F) and slow (A-S) components corresponding to transformations between the input and outputs of variable F and S in (B). The sum of these component filters produces the full ODE filter in (C). (E) In black, the first six seconds of overall model filters for AWC and ASH. In red, the intrinsic neuronal filter L_neuron analytically deconvolved from the overall filter L_meas to remove the dynamical effect of GCaMP3 (Tian et al., 2009; Sun et al., 2013). (F) Normalized power-law nonlinearities for AWC and ASH obtained from individual input-output records (gray) and from trial-averaged input-output records (black). See also Figure S2.

Figure 4. Linear-Nonlinear Models Capture the Dynamics of AWC and ASH Responses

(A) Actual (black) and simulated (red, blue) responses of representative AWC and ASH neurons to fluctuating stimuli. Simulations used a full-parameter L-N model with filters estimated from trial averaged input-output records (blue) or the ODE filter model (red). (B) Performance (%VAF, per cent variance accounted for) of simulated individual trial responses using: (1) full-parameter L-N models estimated from individual trials (2) L-N model estimated from mean trial-averaged records, (3) ODE filter L-N model estimated from individual trials, and (4) ODE filter L-N model estimated from mean trial-averaged records. Cross-val, cross-validated performance of simulating individual trial responses from held-out datasets using: (5) ODE filter L-N model estimated from mean trial-averaged records, (6) trial-averaged L-N model applied to a cross validation set, (7) mean trial-averaged L-N model applied to a hold-out set driven by a second m-sequence stimulus, and (8) mean trial-averaged L-N model estimated from the second m-sequence set tested on trials of the first m-sequence. Bars indicate average performance. n=14 for AWC, n=10 for ASH, and n=7 for both cross-validation sets. (C) In green, simulated responses to trial-averaged 1 s ON/OFF square pulse input records using the mean ODE filter L-N model for AWC and ASH. Actual trial-averaged output records are in black (n=12 for each neuron). Brackets indicate time period excerpted in (D). The ODE models do not describe response magnitude; they were scaled to the peak response. (D) Magnified excerpt of predicted (green) and actual (black) responses in (C) from t=25-35 seconds. Gray vertical lines divide the inset graphs into 2 s epochs aligned to stimulus transitions. (E and F) AWC filters to butanone (1.11×10⁻⁵ M, n=27), and (F) benzaldehyde (9.8×10⁻⁴ M, n=10). Trial-averaged filters are in black and individual trial filters in grey; red is isoamyl alcohol reference. (G) ASH filter for NaCl (500 mM, n=13). Trial-averaged filter is in black and individual trial filters in grey; red is 1 M glycerol reference. See also Figures S4, S5, and S6.

Figure 5. An egl-19 Calcium Channel Mutant Shows Matching Defects in ASH Calcium Dynamics and Behavioral Adaptation.

(A) Average responses of ASH wild type (n=7) and egl-19(n582) (n=23) mutants to 60 s on/off pulses of 1 M Gly, stimulus shown below in red. See also Figures S3 and S5. (B) Excerpt of trial-averaged responses to 200 ms glycerol m-sequences of ASH wild type (n=10), egl-19(n582) reduction-of-function mutants (n=7), and egl-19(ad695gf) gain-of-function mutants (n=7), stimulus shown below in red. (C) Linear filter measured from ASH neurons of egl-19(n582) mutant (upper panel) and egl-19(ad695gf) mutant (lower panel) trial-averaged records (thick lines) and individual records (thin lines) compared to wild-type filter (gray). (D) ODE model filter for ASH from egl-19(n582) mutant (upper panel) and egl-19(ad695gf) mutant (lower panel) trial-averaged records (red, blue) compared to the wild type ODE filter (gray). The egl-19(n582) ODE filter closely matches the ASH wild type fast pathway (A-F) filter from Figure 3D, shown here in green. See also Table S1. (E) Behavioral analysis of egl-19(n582) avoidance responses to 500 mM glycerol. Reversals (black) and omega turns (red) (upper traces) and speed changes (dark blue line, lower traces) are avoidance behaviors elicited upon encountering the high-osmolarity stimulus. Results are shown for seven successive 30 s pulses of glycerol (gray) alternating with 30 s of S Basal buffer (white), with behaviors binned every 2 s. (F,G) The seven pulses from (E) were averaged into a single stimulus-aligned response to 500 mM glycerol (illustrated for WT in left panels) and analyzed in right panels (1420 tracks for WT, 868 tracks for egl-19(n582)). (F) Fraction of animals exhibiting reversal and omega behavior in the last bin of a 30 s pulse period (“adapted”) divided by the maximum fraction exhibiting reversals or omegas in any bin (“peak”) for each of seven consecutive pulses. Error bars indicate s.d., *** P=0.0002, Welch’s two-tailed t-test. (G) Average time to half-recovery of baseline forward speed after glycerol encounter. Error bars indicate s.d., **** P<0.0001, Welch’s two tailed t-test.

Figure 6. The ODR-3 G-alpha Protein Affects The Rapid AWC Filter and Steering During Chemotaxis

(A) Schematic of two distinct chemotaxis strategies in an odor gradient, shown shaded red. A biased random walk turning strategy (top path) consists of bouts of forward runs punctuated by sharp, randomly directed turns (blue dots) whose frequency is modulated by the rate of concentration change. The steering strategy (bottom path) uses gradual, directed corrections to the path direction during forward runs to orient the head toward the gradient. (B) Segment of wild-type and odr-3(n2150) responses to an m-sequence of 9.2×10⁻⁴ M isoamyl alcohol (IAA) and 1.11×10⁻⁵ M butanone (BUT). The stimulus sequence is shown at bottom in red. Note the coarser temporal resolution of the odr-3 IAA response, suggesting that this neuron does not follow stimuli as quickly. (C) Trial-averaged AWC linear filters for wild type and odr-3 responses to IAA and BUT, normalized to peak. Colors match traces in (B). (D) Peak times of individual trial filters, corrected for GCaMP3 kinetics, for wild type and odr-3 responses to IAA and BUT. Colors match traces in (C). For C-D, n=11-27 traces per condition. In (D), WT IAA differs from BUT (P<0.001), WT IAA differs from odr-3 IAA (P<0.001), and WT BT differs from odr-3 BUT (P=0.0013) by Welch’s two-tailed t-test. See also Figure S6. (E) Both WT and odr-3 suppress turning when moving toward the odor (0° bearing) and increase turning when moving away from the odor (180° bearing ), with a positively increasing relationship, the signature of a biased random walk. Turning events were counted across 60 tracks for each strain during a 20 minute assay. Error bars indicate s.e.m. (F) Curving rate versus bearing reveals a defect in steering in odr-3 relative to WT at bearings ≥ 90 degrees. When moving away from the odor source, odr-3 animals curve in the wrong direction. Data are taken from three assays per genotype, with 10-20 animals per assay. Error bars indicate s.e.m. (G) Tracked distance from the head of a worm to an odor source, relative to the mean head-to-source distance along the track segment, versus time. This representative track was made during a forward run at a bearing of 90 degrees to an odor source ∼1 cm away (in red). Bottom plot: “perception signals” simulated by convolving the head position with the empirical trial-average isoamyl alcohol ODE filters for AWC in WT (black) and odr-3 (red). The odr-3 perception signal is attenuated and phase lagged relative to WT.