Persistent Structural Plasticity Optimizes Sensory Information Processing in the Olfactory Bulb

Abstract

In the mammalian brain, the anatomical structure of neural circuits changes little during adulthood. As a result, adult learning and memory are thought to result from specific changes in synaptic strength. A possible exception is the olfactory bulb (OB), where activity guides interneuron turnover throughout adulthood. These adult-born granule cell (GC) interneurons form new GABAergic synapses that have little synaptic strength plasticity. In the face of persistent neuronal and synaptic turnover, how does the OB balance flexibility, as is required for adapting to changing sensory environments, with perceptual stability? Here we show that high dendritic spine turnover is a universal feature of GCs, regardless of their developmental origin and age. We find matching dynamics among postsynaptic sites on the principal neurons receiving the new synaptic inputs. We further demonstrate in silico that this coordinated structural plasticity is consistent with stable, yet flexible, decorrelated sensory representations. Together, our study reveals that persistent, coordinated synaptic structural plasticity between interneurons and principal neurons is a major mode of functional plasticity in the OB.

Figure 1. Development of Adult-Born GC Dendrites

(A) Cartoon of virus injection into the sub-ventricular zone (SVZ) to label adult-born neuroblasts that migrate via the rostral migratory stream (RMS) to the olfactory bulb (OB) becoming GCs that were two-photon imaged through a cranial window. (B) Timeline was as follows: window surgery was performed 1 month prior to virus injection with the mice imaged at a 2-day interval starting at 12 days postinjection (dpi). (C) 2D projections of 3D tracings of an adult-born GC from 12 dpi to 58 dpi. Scale bar, 20 μm. (D) Plot of dendritic length of each traced GC (colors represent different animals). Horizontal long-dashed line: population total dendritic length average (902.9 ± 62.4 μm) taken between 36 to 58 dpi (short-dashed box). Vertical long-dashed line: average time of the initial dendritic growth plateau (23.2 ± 1.4 dpi). (E) Individual tracings (color lines) and combined average dendritic length (black symbol-line) aligned to time at the dpi where the individual dendritic length initially plateaued and each GC normalized to the saturated dendritic length (36 to 58 dpi individual GC average as 100%). Values represent mean ± SEM (F) Example images of dendritic segments at a 2-day interval between 22 and 44 dpi. Scale bar, 10 μm. (G) Plot of percent change in dendrite length (22 dpi = 100%) between 20 to 58 dpi. Values represent mean ± SEM.

Figure 2. Adult-Born GC Developmental Spine Dynamics

(A) Sample images of the same adult-born GC dendritic segment at a 2-day interval for 20–22, 40–42, and 60–62 dpi showing stable (closed arrowheads with numbers indexing stable spines), new (open arrowheads), and lost (asterisks) spines. (B) 3D tracings of the same 60–62 dpi segment as in (A). Scale bar, 4 μm. (C) Plot of dynamics as percent of total average spines between 16–74 dpi with stable (gray triangles), new (blue circles) and lost (red squares) spines. Values represent mean ± SEM. (D) Binned plots of 22–30, 40–48, and 64–72 dpi average spine dynamics at a 2-day interval. Values represent mean ± SEM. ANOVA, **p < 0.05, ***p < 0.01. (E) Image-summed heatmap of five registered tracings of dendritic segments imaged at a 2-day interval overlaid for 22–30, 40–48, and 64–72 dpi ranges showing stable (closed arrowheads) and dynamic (open arrowheads) example spines. Scale bar, 4 μm.

Figure 3. Long-Term Spine Dynamics of Adult-Born GCs

(A) Experimental timeline, mice were injected with virus at postnatal day 70 (P70), window surgeries following at 2, 3, 5, and 13 months postinjection (mpi) with 2-day interval imaging sessions beginning at 3, 4, 6, and 14 mpi, respectively. (B) Sample images of GC dendritic segments at 6 and 14 mpi and of Thy-1-GFP-M mouse motor cortex pyramidal neuron dendritic spines with stable (closed arrowheads with numbers indexing stable spines) and new (open arrowheads) spines. Scale bar, 4 μm. (C) Plot of 2-day interval spine stability of adult-born GCs (red circles, first three data points from Figure 2D) and neocortical pyramidal neurons (blue circle). Values represent mean ± SEM. ANOVA, **p < 0.05, ***p < 0.01.

Figure 4. Adult-Born and Early Postnatal-Born GC Have Similar Spine Dynamics

(A) Experimental outline where early postnatal (P14) mice had virus injections followed by cranial window surgeries at 2–3 mpi and imaging at a 2-day interval at 3–4 mpi. (B) Sample images of adult-born and early post-natal-born GC dendritic segments at a 2-day interval at 3 mpi with stable (closed arrowheads with numbers indexing stable spines), new (open arrowheads) and lost (asterisks) spines. Scale bar, 4 μm. (C) Summary of spine dynamics between adult-born (P70, data from Figures 2D and 3C) and early postnatal-born (P14) groups at 3–4 mpi. Values represent mean ± SEM.

Figure 5. Imaging Teal-Gephyrin Puncta in Mitral and Tufted Cells

(A) Lentiviral injection of floxed-teal-gephyrin into the OB of adult Tbet-Cre mice was performed during the cranial window implantation procedure. Imaging began following a 4-week surgical recovery. (B) 373 × 373 × ~250 μm (xyz) x-projected volume to show dorsal-ventral aspect of labeled mitral and tufted neurons. Scale bar, 50 μm. (C) Morphological tracing of mitral (red) and tufted cells (blue). Scale bar, 50 μm. (D) Classification of all tufted cells (TC) and mitral cells (MC) used in the study based on somatic depth referenced from the mitral cell layer (MCL). (E and F) Example z projections of in vivo imaged mitral cells (E), and tufted cells (F). Scale bar, 20 μm.

Figure 6. Loss of Both New and Stable Puncta Contributes to Similar Dynamics as GC Spines

(A) Example dendritic segments imaged at a 1-day interval, annotated to show examples of stable, new, lost puncta, and solo observations. Scale bar, 5 μm. (B) Cross-sectional profiles (left) and representative images (right; one pixel = 0.182 μm) of average puncta from their first (gray), second (blue), third (red), fourth (orange), fifth (purple), sixth (green) observation, and the average profile of all stable puncta (teal). (C) Logistic growth of puncta peak intensity after puncta appearance (left). Puncta were collected using three different thresholds of detection that considered peaks in the top 0.5%, 1.0%, and 2.0% of ranked pixel intensities. Intensity of stable puncta over eight days of observation (right). The maximum of the logistic fit in new puncta (left) was constrained using the average values at each threshold. Puncta intensities are represented as 8-bit absolute values (0–255 grayscale). (D) Number of stable, new, and lost GC spines and MC and TC puncta at a 1-day interval, as a proportion of total observed spines and puncta (excluding solo observations).

Figure 7. Modeling Functional Aspects of Synapse Turnover

(A) Schematic: two-population model of the OB; structural plasticity is modeled as rewiring of the connection matrix. (B) Example of odor-evoked glomerular afferent response map used as model input (left) and evoked MC output maps generated by a structured (top right) and an unstructured (bottom right) circuit. d_I/O, I/O-distance. (C) Degradation of circuit memory by Poisson synapse turnover quantified by I/O-distances of natural (black trace, mean ± SD, n = 26 networks × 24 stimuli) and of artificial inputs (red trace, mean ± SD, n = 26 networks × 12 stimuli). Initial circuit memory was created using the Hebbian model described in the text. Artificially injected memory yielded similar results (Figure S7A). (D) Spine-age dependent probability of a spine to survive until the next imaging session. Colored bars, experiment (mean ± SEM, n = 13 branches); gray bars, Hebbian model. Dashed line, matching Poisson model (all bars would have the same length). (E) Stability of model responses quantified using Pearson correlation r₆₄ between frames 64 days apart. Note nonlinear (Fisher z) y axis. (F) Effect of spine plasticity on MC output stability for slowly fluctuating input. Box-and-whisker plots; boxes are delineated by quartiles 1–3, whiskers extend 1.5 times interquartile range, cross indicates mean and circles are outliers. Left two columns: Poisson model; input-induced fluctuations (left) are exacerbated (n = 24 odors × 12 networks; p < 10⁻³⁰; Wilcoxon rank sum test) by spine turnover (right). Right two columns: Hebbian model; input-induced fluctuations (left) are partially compensated for (n = 12 odors × 16 networks; p < 10⁻³⁰; Wilcoxon rank sum test) by spine turnover (right). (G) Average pattern correlation r_0.6 between responses to related odors (input correlation r > 0.6) decreases with the exposure to these odors. Note non-linear (Fisher z) y axis. Inset, blow-up for early times. (H) Overlap (scalar product) of response profiles between different input channels (“glomeruli”). Lighter intensity indicates higher overlap. (I) Steady-state disynaptic MC-to-MC inhibition, excluding diagonal (self-inhibition). Lighter color indicates stronger weight. (J) Same data (off-diagonal elements) as in (H) and (I). MCs associated with glomeruli with overlapping response profiles are preferentially connected (r = 0.678) in the Hebbian (black dots, left y axis) but not the Poisson (gray dots, right y axis) model. Parameters of computer model: k_MC = 20 synapses per GC, unitary synaptic weight w = 8.125 × 10⁻⁵. Poisson model only, probability of synapse replacement per update, 0.019. Hebbian model only, GC plasticity threshold G_min = 16.0, resilience threshold R₀ = 1.015, resilience threshold sharpness γ = 50.0, resilience update rate λ_R = 0.01.