Idiosyncratic calcium dynamics predict individual odor preferences

(A) Olfactory circuit schematic. Olfactory receptor neurons (ORNs, peach outline) and projection neurons (PNs, plum outline) are comprised of ∼51 classes corresponding to odor receptor response channels. ORNs (gray shading) sense odors in the antennae and synapse on dendrites of PNs of the same class in ball-shaped structures called glomeruli located in the antennal lobe (AL). Local neurons (LNs, green outline) mediate interglomerular cross-talk and presynaptic inhibition, amongst other roles (Olsen and Wilson, 2008; Yaksi and Wilson, 2010). Odor signals are normalized and whitened in the AL before being sent to the mushroom body and lateral horn for further processing. Schematic adapted from Honegger et al., 2019 (B) Experiment outline. (C) Odor preference behavior tracking setup (reproduced from Honegger, Smith, et al. (Honegger et al., 2019)) and example individual fly ethograms. OCT (green) and MCH (magenta) were presented for 3 minutes. (D) Head-fixed 2-photon calcium imaging and odor delivery setup (reproduced from Honegger et al., 2019) (E) Orco and GH146 driver expression profiles (left) and example segmentation masks (right) extracted from 2-photon calcium images for a single fly expressing Orco>GCaMP6m (top, expressed in a subset of all ORN classes) or GH146>Gcamp6m (bottom, expressed in a subset of all PN classes). (F) Time-dependent Δf/f for glomerular odor responses in ORNs (peach) and PNs (plum) averaged across all individuals: DC2 to OCT (upper left), DM2 to OCT (upper right), DC2 to MCH (lower left), and DM2 to OCT (lower right). Shaded error bars represent S.E.M. (G) Peak Δf/f for each glomerulus-odor pair averaged across all flies. (H) Individual neural responses measured in ORNs (left) or PNs (right) for 50 flies each. Columns represent the average of up to 4 odor responses from a single fly. Each row represents one glomerulus-odor response pair. Odors are the same as in panel (G). (I) Principal component analysis of individual neural responses. Fraction of variance explained versus principal component number (left). Trial 1 and trial 2 of ORN (middle) and PN (right) responses for 20 individuals (unique colors) embedded in PC 1-2 space. (J) Euclidean distances between glomerulus-odor responses within and across flies measured in ORNs (n=65 flies) and PNs (n=122 flies). Distances calculated without PCA compression. Points represent the median value, boxes represent the interquartile range, and whiskers the range of the data. (K) Bootstrapped R2 of OCT-AIR preference prediction from each of the first 5 principal components of neural activity measured in ORNs (top, all data) or PNs (bottom, training set). (L) Measured OCT-AIR preference versus preference predicted from PC 1 of ORN activity (n=30 flies). (M) Measured OCT-AIR preference versus preference predicted from PC 1 of PN activity in n=53 flies using a model trained on a training set of n=18 flies (see Figure 2 – figure supplement 1A-B for train/test flies analyzed separately). (N) Bootstrapped R2 of OCT-MCH preference prediction from each of the first 5 principal components of neural activity measured in ORNs (top, all data) or PNs (bottom, training set). (O) Measured OCT-MCH preference versus preference predicted from PC 1 of ORN activity (n=35 flies). (P) Measured OCT-MCH preference versus preference predicted from PC 2 of PN activity in n=69 flies using a model trained on a training set of n=47 flies (see Figure 2 – figure supplement 1C-D for train/test flies analyzed separately). Shaded regions in L,M,O,P are the 95% CI of the fit estimated by bootstrapping.

Variation in global and relative glomerular responses explains individual preferences

(A) PC 1 loadings of PN activity for flies tested for OCT-AIR preference (n=53 flies). (B) Interpreted PN PC 1 loadings. (C) Measured OCT-AIR preference versus preference predicted by the average peak response across all PN coding dimensions (n=53 flies). (D) PC 1 loadings of ORN activity for flies tested for OCT-AIR preference (n=30 flies). (E) Interpreted ORN PC 1 loadings. (F) Measured OCT-AIR preference versus preference predicted by the average peak response across all ORN coding dimensions (n=30 flies). (G) PC 2 loadings of PN activity for flies tested for OCT-MCH preference (n=69 flies). (H) Interpreted PN PC 2 loadings. (I) Measured OCT-MCH preference versus preference predicted by the average peak PN response in DM2 minus DC2 across all odors (n=69 flies). (J) Yoked control experiment outline and example behavior traces. Experimental flies are free to move about tunnels permeated with steady state OCT and MCH flowing into either end. Yoked control flies are delivered the same odor at both ends of the tunnel which matches the odor experienced at the nose of the experimental fly at each moment in time. (K) Imposed odor experience versus the odor experience predicted from PC 2 of PN activity (n=27 flies) evaluated on the model trained from data in Figure 1P. Shaded regions in C,F,I,K are the 95% CI of the fit estimated by bootstrapping.

Idiosyncratic presynaptic marker density in DM2 and DC2 predicts OCT-MCH preference

(A) Experiment outline. (B) Example slice from a z-stack of the antennal lobe expressing Orco>Brp-Short (green) with DC2 and DM2 visible (white dashed outline). nc82 counterstain (magenta). (C) Example glomerulus segmentation masks extracted from an individual z-stack. (D) Bootstrapped R2 of OCT-MCH preference prediction from each of the first 4 principal components of Brp-Short density measured in ORNs (training set, n=22 flies). (E) PC 2 loadings of Brp-Short density. (F) Measured OCT-MCH preference versus preference predicted from PC 2 of ORN Brp-Short density in n=53 flies using a model trained on a training set of n=22 flies (see Figure 3 – figure supplement 1 for train/test flies analyzed separately). (G) Measured OCT-MCH preference versus preference predicted from ORN Brp-Short density in DM2 minus DC2 (n=53 flies).

Loci of individuality across the olfactory periphery

(A) Table summarizing circuit element predictors, the strength of their nominal correlation with odor-vs-air behavior scores, and the inferred correlation between latent calcium / latent behavior. See analysis in Figure 4 – figure supplement 1. Schematic at right places these values in the context of the olfactory circuit. ORN Ca++ corresponds to PC 1 of ORN calcium (Figure 1L), PN Ca++ corresponds to PC1 of PN calcium (Figure 1M; trained model applied to train+test data). (B) As in (A) but for odor-vs-odor experiments. ORN Ca++ corresponds to PC 1 of ORN calcium (Figure 1O), ORN pre-synapse density corresponds to PC2 of Brp-Short relative fluorescence (Figure 3F; trained model applied to train+test data), PN Ca++ corresponds to PC 2 of PN calcium (Figure 1P; trained model applied to train+test data).

Calcium & Brp-Short – behavior model statistics

Simulation of developmentally stochastic olfactory circuits

(A) AL modeling analysis outline. (B) Leaky-integrator dynamics of each simulated neuron. When a neuron’s voltage reaches its firing threshold, a templated action potential is inserted, and downstream neurons receive a postsynaptic current. See Antennal Lobe modeling in Materials and Methods. (C) Synaptic weight connectivity matrix, derived from the hemibrain connectome (Scheffer et al., 2020). (D) Spike raster for randomly selected example neurons from each AL cell type. Colors indicate ORN/PN glomerular identity and LN polarity (i=inhibitory, e=excitatory). (E) Schematic illustrating sources of developmental stochasticity as implemented in the simulated AL framework. See Supplementary Video 4 for the effects of these resampling methods on the synaptic weight connectivity matrix. (F) PN glomerulus-odor response vectors for 8 idiosyncratic ALs subject to Input spike Poisson timing variation, PN input synapse density resampling, and ORN and LN population bootstrapping. (G) Loadings of the principal components of PN glomerulus-odor responses as observed across experimental flies (top). Dotted outlines highlight loadings selective for the DC2 and DM2 glomerular responses, which underlie predictions of individual behavioral preference. (H-K) As in (G) for simulated PN glomerulus-odor responses subject to Input spike Poisson timing variation, PN input synapse density resampling, and ORN and LN population bootstrapping. See Figure 5 – figure supplement 5 for additional combinations of idiosyncrasy methods. In (F-K) the sequence of odors within each glomerular block is: OCT, 1-hexanol, ethyl-lactate, 2-heptanone, 1-pentanol, ethanol, geranyl acetate, hexyl acetate, MCH, pentyl acetate and butanol.

Typical electrophysiology features of AL cell types, used as model parameters

FRmax, the maximum ORN firing rate, was set to 400 Hz. Dglom, odor is a value between 0 and 1 from the DoOR database, representing the response of an odorant receptor/glomerulus to an odor, estimated from electrophysiology and/or fluorescence data (Münch and Galizia, 2016). ORNs display adaptation to odor stimuli (Wilson, 2013), captured by the final term with timescale ta = 110 ms to 75% of the initial value, as done in Kao and Lo, 2020. Thus, the functional maximum firing rate of an ORN was 75% of 400 Hz = 300 Hz, matching the highest ORN firing rates observed experimentally (Hallem et al., 2004). After determining the times of ORN spikes according to this firing-rate rule, spikes were induced by the addition of 106 picoamps in a single time step. This reliably triggered an action potential in the ORN, regardless of currents from other neurons. In the absence of odors, spike times for ORNs were drawn by a Poisson process at 10 Hz, to match reported spontaneous firing rates (de Bruyne et al., 2001).

Behavioral measurements and individual preference persistence

(A) Behavioral measurement apparatus (adapted from Honegger et al., 2019) (B) Odor preference persistence over 3 hours for flies given a choice between 3-octanol and air (n=34 flies). (C) Odor preference persistence over 24 hours for flies given a choice between 3-octanol and air (n=97 flies). (D) Odor preference persistence over 3 hours for flies given a choice between 3-octanol and 4-methylcyclohexanol (n=51 flies). (E) Odor preference persistence over 24 hours for flies given a choice between 3-octanol and 4-methylcyclohexanol (n=49 flies).

Average glomerulus-odor time-dependent responses

Time-dependent responses of each glomerulus identified in our study to the 13 odors in our odor panel. Data represents the average across flies (ORN, peach curves, n=65 flies; PN, plum curves, n=122 flies). Shaded error bars represent S.E.M.

Individual glomerulus-odor responses

Idiosyncratic odor coding measured in ORNs (left, n=65 flies) and PNs (right, n=122 flies). Each column represents the response (max Δf/f attained over the odor trial) of a single fly averaged over up to 4 odor deliveries. Each row represents a glomerulus-odor response pair. Missing data are indicated in gray.

Glomerulus responses and identification

(A) Glomerulus odor responses measured in PNs versus those measured in ORNs. Points correspond to the odorants listed in Figure 1G. (B) Cross-odor trial correlation matrix between glomerular odor responses in ORNs and PNs. (C) Peak calcium responses for each glomerulus-odor pair measured in this study plotted against those recorded in the DoOR dataset (Münch and Galizia, 2016). (D) Peak calcium responses for each individual glomerulus plotted against those recorded in the DoOR dataset.

Idiosyncrasy of ORN and PN responses

(A) Logistic regression classifier accuracy of decoding individual identity from individual odor panel peak responses. PCA was performed on population responses and the specified fraction of variance (x-axis) was retained. Individual identity can be better decoded from PN responses than ORN responses in all cases. (B) Individual trial-to-trial glomerulus-odor responses embedded in PC 1-2 space. Responses for the same flies as Figure 1I are shown. Each linked color represents one fly. Trial 1 and trial 2 responses are shown for ORN left lobe (upper left), ORN right lobe (upper right), PN left lobe (lower left), and PN right lobe (lower right). (C) Distance in the full glomerulus-odor response space between recordings within a lobe (trial-to-trial), across lobes (within fly), and across flies for ORNs and PNs. Points represent the median value, boxes represent the interquartile range, and whiskers the range of the data.

Calcium response correlation matrices

Correlation between calcium response dimensions across flies measured in ORNs (top) and PNs (bottom). Glomerulus-odor responses are correlated at the level of glomeruli in both cell types. Inter-glomerulus correlations are more prominent in ORNs than PNs, consistent with known AL transformations that result in decorrelated PN activity (Bhandawat et al., 2007; Luo et al., 2010).

Calcium imaging principal component loadings

(A-B) First 10 principal component loadings measured from calcium responses in ORNs (A, n=65 flies) and PNs (B, n=122 flies). Loadings are grouped by glomerulus, with each loading within a glomerulus representing the response of that glomerulus to one odor in the odor panel. Odors are the same as those listed in Figure 1G. (C-D) The same 10 principal component loadings as those shown in panels (A-B) grouped by odor rather than glomerulus. Glomeruli within each odor block are given in the order of panels (A) and (B).

Measured preference vs. PN activity-based predicted preference, split by training/testing set

(A) Measured OCT-AIR preference versus preference predicted from PC 1 of PN activity in a training set (n=18 flies). (B) Measured OCT-AIR preference versus preference predicted from PC 1 on PN activity in a test set (n=35 flies) evaluated on a model trained on data from panel (A). (C) Measured OCT-MCH preference versus preference predicted from PC 2 of PN activity in a training set (n=47 flies). (D) Measured OCT-MCH preference versus preference predicted from PC 2 on PN activity in a test set (n=22 flies) evaluated on a model trained on data from panel (C).

ORN>Brp-Short characterization and model predictions

(A-C) Right versus left glomerulus properties measured from z-stacks of stained Orco>Brp-Short samples: (A) Volume, (B) total Brp-Short fluorescence, (C) Brp-Short fluorescence density. (D-F) Same data as panels (A-C) represented in violin plots (kernel density estimated). (G) Principal component loadings of Brp-Short density calculated using only training data (n=22 flies). (H) Principal component loadings of Brp-Short density calculated using all data (n=53 flies). (I) Measured OCT-MCH preference versus preference predicted from PC 2 of ORN Brp-Short density in a training set (n=22 flies). (J) Measured OCT-MCH preference versus preference predicted from PC 2 on ORN Brp-Short density in a test set (n=31 flies) evaluated on a model trained on data from panel (I). (K) Example expanded AL expressing Or13a>Brp-Short (left) and Imaris-identified puncta from that sample (right). (L) OCT-MCH preference score plotted against Brp-Short puncta density in expanded Or13a>Brp-Short samples (n=8 flies). (M) OCT-MCH preference score plotted against Brp-Short median puncta volume in expanded Or13a>Brp-Short samples (n=8 flies). Shaded regions in F,G,I,J are the 95% CI of the fit estimated by bootstrapping.

Calcium and Brp-Short predictor variation

(A) Histogram of average PN Δf/f across all coding dimensions in flies in which OCT-AIR preference was measured (top) and OCT-AIR preference versus average PN Δf/f (n=53 flies) (bottom). (B) Similar to (A) for ORN Δf/f and OCT-AIR preference (n=30 flies). (C) Similar to (A) for Δf/f difference between DM2 and DC2 PN responses and OCT-MCH preference (n=69 flies). (D) Similar to (A) for % Brp-Short density difference between DM2 and DC2 ORNs and OCT-MCH (n=53 flies).

Estimating latent calcium - behavior correlations

(A) Schematic of inference approach to estimate the correlation between latent calcium (c) and behavioral (b) states (⍴signal). This method can be applied identically to infer ⍴signal between Brp measurements and behavior. (B) Demonstration of ⍴signal inference for OCT vs MCH models presented in Figure 4: ORN calcium PC 1 (left, N=30, R2=0.25 indicated in dashed line), ORN Brp-Short PC 2 from trained model applied to train+test data (middle, N=53, R2=0.088 indicated in dashed line), PN calcium PC 2 from trained model applied to train+test data (right, N=69, R2=0.20). Black line indicates median Rc,b2 (Rbrp,b2 for Brp-Short model) among the 10,000 simulations for each ⍴signal, shaded areas (from lightest to darkest to lightest) indicate 5-15th, 15-25th, …, 85-95th percentile R2 (R2). The marginal distribution for ⍴ was estimated as the distribution of simulations for each ⍴signal for which the simulated R2 (R2) had a value +/-20% of the linear models’ R2 (dashed lines). For the examples depicted here, the median ⍴signal for ORN calcium PC1 was 0.30 (90% CI as estimated by the 5th-95th percentiles of the marginal distribution: 0.02-0.74), for ORN Brp-Short PC 2: 0.50 (0.11-0.85), for PN PC 2: 0.75 (0.44-0.96).

Time-dependent preference- and odor-decoding

(A) R2 of odor-vs-air preference predicted by PC 1 of PN activity as a function of time across trials (n=53 flies). (B) R2 of odor-vs-air preference predicted by PC 1 of ORN activity as a function of time across trials (n=30 flies). (C) R2 of odor-vs-odor preference predicted by PC 2 of PN activity (solid plum, n=69 flies) or PC 1 of ORN activity (dashed peach, n=35 flies) as a function of time across trials. (D) Logistic regression classifier accuracy of decoding odor identity from 5 glomerular responses as a function of time. Dashed curves indicate performance on shuffled data.

AL model raster plot

(A) Action potential raster plot of ORNs in the baseline simulated AL. Rows are individual ORNs, black ticks indicate action potentials. Random shades of orange at left indicate blocks of ORN rows projecting to the same glomerulus. (B) The remaining neurons in the model. Shades of green indicate excitatory vs inhibitory LNs and shades of purple indicate PNs with dendrites in the same glomeruli.

AL model baseline outputs compared to experimental data

(A) Distributions of model neuron firing rates by cell type across odors (transparent black points are individual neuron-odor combinations). Black lozenge symbols indicate the mean firing rate of the points to the right. Yellow stars indicate the comparable experimental values reported in (Chou et al., 2010; de Bruyne et al., 2001; Nagel et al., 2015; Wilson, 2004). (B) Scatter plots of average PN firing rate vs ORN firing rate during odor stimuli in the model vs experimental values (Bhandawat et al., 2007). Points are odors, colors are glomeruli. (C) Histograms of ON odor minus OFF odor glomerulus-average PN and ORN firing rates in the model vs experimental values (Bhandawat et al., 2007), showing flatter distributions in PNs. (D) Odor representations in the first 2 PCs of glomerulus-average ORN responses and PN responses in the model and experimental results (Bhandawat et al., 2007). Points are odors. Pairwise distances between PN representations are more uniform than in ORNs in both the model and experimental data. Panels (B)-(D) use glomerulus-average PN and ORN firing rates from six of the seven glomeruli in Bhandawat et al., 2007, as VM2 is significantly truncated in the hemibrain (Scheffer et al., 2020). Literature features in panels (B)-(D) were extracted from Bhandawat et al., 2007 using WebPlotDigitizer (Rohatgi, 2021).

Sensitivity analysis of aORN, aeLN, aiLN, aPN parameters

(Left, blue to red colormap): magnitude of parameter manipulation. (Center, dark blue to yellow colormap): mean glomerular firing rate (Hz) responses of PNs (DL1, DM1, DM2, DM3, DM4, VA2) to 11 odors (order within each glomerulus (colored bands at top): 3-octanol, 1-hexanol, ethyl lactate, 2-heptanone, 1-pentanol, ethanol, geranyl acetate, hexyl acetate, 4-methylcyclohexanol, pentyl acetate, 1-butanol, 3-octanol). (Right, pink to green colormap): manipulation effect size on mean PN-odor responses (Cohen’s d). (Top): baseline parameter set. (Middle): single-parameter manipulations from 1/4x to 4x. (Bottom): multiple-parameter manipulations. For further detail see AL model tuning in Materials and Methods. No manipulations yielded effect sizes larger than 0.9; aPN is the most sensitive parameter.

Synapse counts vs glomerular volume in the hemibrain and AL model

(A) Left) Scatter plot of total PN input synapses within a glomerulus vs that glomerulus’ volume from the hemibrain data set. Solid line represents the maximum likelihood-fit mean synapse count vs glomerular volume, and dashed lines the fit +/-1 standard deviation. Middle) As (left) but for a single sample from the parameterized distribution of PN input synapses vs glomerular volume. Right) As in previous for a single bootstrap resample of PNs. Color-highlighted glomeruli illustrate that when PNs within a glomerulus have highly asymmetrical synapse counts, bootstrapping them alone can result in apparent synapse densities that lie outside the empirical distribution (left). (B) As in (A) but on log-log axes, showing the linear relationship between synapse density and glomerular volume after this transformation, and bootstrapped densities falling outside this distribution at right.

PN response PCA loadings under various sources of circuit idiosyncrasy

(A) Loadings of the principal components of PN glomerulus-odor responses as simulated across AL models where Gaussian noise with a standard deviation equal to 0, 20, 50, and 100% of each synapse weight was added to each synaptic weight in the hemibrain data set. (B) circuit variation coming from bootstrapping of each major AL cell type or all three simultaneously. (C) circuit variation coming from bootstrap resampling of different cell-type combinations in addition to PN input synapse density resampling as illustrated in Figure 5 – figure supplement 4.

Classifiability of simulated idiosyncratic behavior under different sources of circuit idiosyncrasy

Simulated PN odor-glomerulus firing rates projected into their first 3 principal components. Individual points represent single runs of resampled AL models, under four different sources of idiosyncratic variation. PN responses in all odor-glomerulus dimensions were used to calculate simulated behavior scores for each resampled AL, by applying the PN calcium-odor-vs-odor linear model (Figure 2G). Magenta points represent flies with simulated preference for MCH in the top 50%, and green OCT preference. % Misclassification refers to 100% – the accuracy of a linear classifier trained on MCH-vs-OCT preference in the space of the first three PCs. This measures how much of the variance along the PN calcium-odor-vs-odor linear model lies outside the first three PCs of simulated PN variation.