Environmental enrichment enhances patterning and remodeling of synaptic nanoarchitecture as revealed by STED nanoscopy

  1. Waja Wegner
  2. Heinz Steffens
  3. Carola Gregor
  4. Fred Wolf
  5. Katrin I Willig  Is a corresponding author
  1. Optical Nanoscopy in Neuroscience, Center for Nanoscale Microscopy and Molecular Physiology of the Brain, University Medical Center Göttingen, Germany
  2. Max Planck Institute for Multidisciplinary Sciences, City Campus, Germany
  3. Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Germany
  4. Department of Optical Nanoscopy, Institut für Nanophotonik Göttingen e.V., Germany
  5. Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Germany
  6. Max Planck Institute for Dynamics and Self-Organization, Germany
  7. Göttingen Campus Institute for Dynamics of Biological Networks, Germany

Abstract

Synaptic plasticity underlies long-lasting structural and functional changes to brain circuitry and its experience-dependent remodeling can be fundamentally enhanced by environmental enrichment. It is however unknown, whether and how the environmental enrichment alters the morphology and dynamics of individual synapses. Here, we present a virtually crosstalk-free two-color in vivo stimulated emission depletion (STED) microscope to simultaneously superresolve the dynamics of endogenous PSD95 of the post-synaptic density and spine geometry in the mouse cortex. In general, the spine head geometry and PSD95 assemblies were highly dynamic, their changes depended linearly on their original size but correlated only mildly. With environmental enrichment, the size distributions of PSD95 and spine head sizes were sharper than in controls, indicating that synaptic strength is set more uniformly. The topography of the PSD95 nanoorganization was more dynamic after environmental enrichment; changes in size were smaller but more correlated than in mice housed in standard cages. Thus, two-color in vivo time-lapse imaging of synaptic nanoorganization uncovers a unique synaptic nanoplasticity associated with the enhanced learning capabilities under environmental enrichment.

Editor's evaluation

Synapses mediate information transmission in the brain, and part of the synaptic structure called spines are the receiving end of signal transfer between neurons. Using a custom-built superresolution microscope, the study reveals the nanoscale structural dynamics of individual spine shape and its resident scaffolding protein PSD95 simultaneously, in mouse cortex in vivo. Aspects of the structural dynamics are found to differ depending on whether mice have been reared in a simple housing or in an enriched environment, the latter condition being associated with enhanced activity.

https://doi.org/10.7554/eLife.73603.sa0

Introduction

Activity-driven changes of spine dynamics

Over an entire lifespan, cognitive, sensory, and motor learning is associated with changes to a specific assembly of synapses which is often termed the memory engram (Poo et al., 2016). Thus, spines emerge, disappear, or change with cellular processes underlying learning, and even ‘remember’ previous sensory experience (Hofer et al., 2009). There is also evidence that learning induces structural and functional synaptic changes similar to long-term potentiation (LTP) protocols (Poo et al., 2016). In this concept of learning, the maintenance of memory critically depends on the stability of the underlying synaptic connections. Synaptic structures, however, are highly volatile intrinsically as such that synaptic connections undergo continuous spontaneous remodeling without any activity (Mongillo et al., 2017; Ziv and Brenner, 2018); for example, intact synapses are formed despite abolishing pre-synaptic release (Sigler et al., 2017) or network silencing (Hazan and Ziv, 2020). Due to spatial resolution constraints, previous in vivo studies mostly focused on the persistency of spines and synapses in terms of their appearance and elimination; the spine size was estimated from fluorescence intensity. Therefore, directly assessed activity-driven changes in synapse or spine head size in vivo are missing.

Spine to synapse structural correlation

Decades of neuroscience research has shown a tight correlation between structural plasticity, the morphological transformation of spines and synapses, and modifications in synaptic transmission, termed functional plasticity (Yuste and Bonhoeffer, 2001), which was confirmed recently at ultrastructural resolution (Holler et al., 2021). Functional changes are linked to anchoring amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPAR) via transmembrane AMPAR-regulatory proteins (TARPs) to the post-synaptic density (PSD) scaffolding protein PSD95 (Compans et al., 2016; Herring and Nicoll, 2016). AMPAR-mediated currents were shown to increase simultaneously with the spine head directly after LTP induction. Recent evidence suggests that not all synaptic structures follow this fast dynamic; as such, accumulations of PSD95 increase in vitro with a delay of ~1 hr after inducing LTP, whereas the spine heads increase in less than a minute (Bosch et al., 2014; Meyer et al., 2014). Thus, the relation between spine size and PSD organization (Arellano et al., 2007; Cane et al., 2014; Harris et al., 1992; Meyer et al., 2014) may be temporally dynamic and complex during synaptic change and the remodeling of PSD and spine head plasticity might be more independent than previously thought. Here, we investigate the structural correlation at nanoscale of the spine and post-synapse in vivo using mice reared in enriched environment representing increased activity conditions and normal housing representing baseline conditions.

PSD95 nanoorganization

By applying superresolution microscopy we and others have shown that PSD95 is often arranged in clusters, rings, or a more complex nanopattern, and that this nanoorganization undergoes pronounced intrinsic structural changes on the time scale of minutes to hours (Hruska et al., 2018; MacGillavry et al., 2013; Wegner et al., 2018). Recently, it was shown that such a sub-synaptic nanoorganization exists at the pre- and post-synaptic site and that both are aligned in the so-called nanocolumns (Hruska et al., 2018; Tang et al., 2016). Computational studies show that changes of the shape of the PSD (Franks et al., 2003) or modest clustering of AMPAR (Savtchenko and Rusakov, 2014) is a highly efficient way to modify synaptic strength without altering the total amount of receptors. Moreover, AMPAR current amplitude drops significantly already at 50 nm offset between pre-synaptic glutamate release site and AMPAR cluster (Haas et al., 2018). Therefore, activity-driven structural rearrangement at the nanoscale to align post-synaptic receptors to pre-synaptic release sites may be faster than the incorporation of new molecules to induce changes in the synaptic strength.

This study

Here, we apply nanoscale stimulated emission depletion (STED) microscopy to map temporal changes of the spine head and PSD95 accumulations simultaneously in vivo, and address (1) the plasticity of spine heads and synapses at baseline at time scales similar to LTP processes; (2) the correlation between PSD95 and spine head size changes; (3) the plasticity of the PSD95 nanoorganization; and (4) whether enhanced activity modifies the structure and/or plasticity of these measures. For this purpose, we employ environmental enrichment (EE) where a mouse is provided with multi-sensory stimulation, cognitive activity, social interactions, and physical exercise, which modifies the degree of plasticity and dynamics of cortical sensory circuits. EE has been shown to accelerate the maturation of new neurons, increase pre- and post-synaptic protein levels (Nithianantharajah et al., 2004), and facilitate synaptic plasticity (Greifzu et al., 2014; Nithianantharajah and Hannan, 2006). Structural changes include increased dendritic branching and spine density (Gelfo et al., 2009; Leggio et al., 2005) and induction of spine formation (Yang et al., 2009). However, it is unknown whether the effects of EE leave their mark only at the level of spine formation and elimination such as observed by Yang et al., 2009, or whether EE also affects the dynamics and nanostructure of all individual spines and PSDs.

To unambiguously assess these relationships between spine morphology and PSD95, we set up a virtually crosstalk-free two-color STED microscope for in vivo superresolution imaging of EGFP and Citrine. We utilized a transcriptionally regulated antibody-like protein to visualize endogenous PSD95 without introducing overexpression artifacts (Gross et al., 2013). Together with a membrane label, we simultaneously recorded temporal morphological changes of the spine head and PSD95 nanoorganization and compared its dynamics between mice raised in EE and in standard cages (control, Ctr). By investigating the synaptic dynamics in the mouse visual cortex, a brain region known to exhibit enhanced plasticity under EE conditions (Baroncelli et al., 2010; Kalogeraki et al., 2017) and accessible to light microscopy, we demonstrate that spine head and PSD95 size distributions decrease in variability while spine heads increase in average size under EE. In addition, PSD95 underwent much stronger directional changes in size and reorganization of their nanoorganization under EE, while the average change in amplitude was smaller compared to the controls. Dynamical changes of PSD95 and spine head size correlated only mildly.

Results

Virtually crosstalk-free two-color STED Microscope

To unambiguously dissect the dynamics of distinct synaptic components and examine their interactions, we established a virtually crosstalk-free two-color STED microscope for imaging of EGFP and EYFP or Citrine, respectively. The challenge for in vivo two-color STED microscopy is to find an in vivo compatible pair of fluorescent molecules with similar emission spectra so that it can be depleted with the same STED beam, but at the same time can be temporally or spectrally separated (Willig et al., 2021). Previous attempts with STED microscopy of EGFP and EYFP utilized a single excitation wavelength and two-color detection, which suffered from high crosstalk, and therefore required a linear unmixing of the channels (Tønnesen et al., 2011). Channel unmixing, however, requires large signal-to-noise levels. To reduce crosstalk and thus avoid the necessity of channel unmixing, we extended our previously described in vivo STED microscope (Willig et al., 2014) by an additional two-color excitation and detection to selectively excite the green or yellow fluorescent protein (Figure 1A). We utilized 483 nm pulses to excite EGFP and detected its spontaneous fluorescence at 498–510 nm (Det1, Figure 1B). EYFP (or Citrine) was excited at 520 nm and detected mainly at 532–555 nm (Det2, Figure 1B). The 483 and 520 nm excitations were switched on and off alternately while recording two consecutive line scans to temporally separate the EGFP and EYFP/Citrine emissions. To determine the crosstalk between both channels, we investigated one-color labeled cells expressing EGFP or EYFP. Live-cell imaging showed that we have designed a virtually crosstalk-free microscope with only ~8% crosstalk of EYFP in the EGFP channel (channel 1) and ~5% of the EGFP signal in the EYFP channel (channel 2) which does not require linear unmixing (Figure 1C). STED was performed by a 595 nm laser beam passing a vortex phase plate to create a donut-shaped intensity profile in the focal plane of the objective as previously described (Eggeling et al., 2015).

Figure 1 with 1 supplement see all
Virtually crosstalk-free two-color in vivo stimulated emission depletion (STED) microscopy.

(A) Custom-designed in vivo STED microscope with pulsed 483 nm (Exc1) and 520 nm (Exc2) excitation and 595 nm STED laser. APD: avalanche photon detector, BP: bandpass filter, Det: detection, DM: dichroic mirror, OPO: optical parametric oscillator, QWP: quarter wave plate, VPP: vortex phase plate. See Material and methods section for details. (B) Excitation (dashed line) and emission (solid line) spectrum of EGFP and EYFP and wavelength regions for selective excitation and detection. (C) Cultured hippocampal neurons expressing the actin marker Lifeact (LA)-EGFP or LA-EYFP. Excitation of EGFP at 483 nm close to its excitation maximum (B) and detection at 498–510 nm (Det1) reduced the crosstalk to 5% in channel 2 (C). EYFP or Citrine was excited at 520 nm close to its maximum (B) and detected at 532–555 nm (Det2) which resulted in a low crosstalk of 8% in channel 1 (C). (D) In vivo STED microscopy image of apical dendrite in layer 1 of the visual cortex of an anesthetized mouse. Labeling of membrane (myr-EGFP-LDLR(Ct)) and PSD95 (PSD95.FingR-Citrine) visualized the spine morphology and PSD95 nanoorganization at superresolution (D, inset). Images are smoothed and represent maximum intensity projections (MIPs). No unmixing was performed due to the low crosstalk.

Two-color in vivo STED microscopy of endogenous PSD95 and spine morphology

Having established the virtually crosstalk-free detection of two STED approved fluorescent proteins (Nägerl et al., 2008; Willig et al., 2006), we set out to simultaneously superresolve the nanoorganization of the PSD protein PSD95 and the associated spine morphology in vivo. To this end, we generated recombinant adeno-associated viral (AAV) particles encoding fusion proteins under control of the human Synapsin promoter (hSyn). To visualize PSD95, we expressed the antibody-like protein PSD95.FingR, which has been shown to label endogenous PSD95 (Gross et al., 2013; Willig et al., 2021) attached to the fluorescent protein Citrine. The expression of PSD95.FingR-Citrine is controlled by a transcriptional regulation system that prevents expression after saturation of the binding sites and therefore reduces background (Gross et al., 2013). For spine morphology, we expressed myr-EGFP-LDLR(Ct), a combination of a myristoylation site (myr), EGFP, and the C-terminal (Ct) cytoplasmic domain of low-density lipoprotein receptor (LDLR), a potent marker for the dendritic membrane (Kameda et al., 2008; Willig et al., 2021). In order to reduce the density of labeled neurons and thus overlapping fluorescence signals, the fluorescent labels were incorporated into AAVs in reverse orientation in the vector between double-floxed inverted open reading frames (DIO). Expression of the labels was enabled by Cre recombinase encoding AAVs co-injected at low concentration (Willig et al., 2021). Hence, the density of labeled neurons was tuned by the dilution of the Cre expressing AAV and the brightness of the PSD95 and membrane labeling by the concentration of the respective AAVs; thus the density of labeled cells and their brightness were independent variables. We co-injected the three AAVs into pyramidal cell layer 5 of the visual cortex. Three to six weeks after transduction, the mice were anesthetized and a cranial window was inserted over the visual cortex. To perform motion and aberration-free imaging at nanoscale resolution, the cranial window needs to be of highest quality. As described in detail in Steffens et al., 2020, critical steps involve a craniotomy that is as atraumatic as possible and does not damage the cortical surface when drilling or removing the bone plate and dura mater. Moreover, the gap between the brain surface and the cover glass needs to be negligibly small and the right choice of the dental cement is important to avoid bending of the cover glass. With such an optimized cranial window, STED microscopy visualized dendrites, spines, and attached PSD95 assemblies in layer 1 of the visual cortex at superresolution (Figure 1C) with a resolution of at least ~70 nm for Citrine and ~84 nm for EGFP (Figure 1—figure supplement 1A, B). The inset in Figure 1D reveals a perforated nanoorganization of endogenous PSD95 that would not be detectable with conventional in vivo light microscopy due to the insufficient resolution. Due to the low crosstalk, a computational post-processing such as unmixing was not required. We observed mainly dendrites that expressed both, EGFP and Citrine, and only rarely found dendrites expressing either Citrine or EGFP alone.

EE housed mice exhibit less variability in the size of spine head and PSD95 assemblies while their heads are larger than in control

Having established the two-color in vivo STED microscopy, we addressed the question of whether spines or PSDs exhibit systematic differences in size between EE and Ctr mice. EE mice were housed in a large, two-floor cage equipped with three running wheels for physical exercise, a labyrinth for cognitive stimulation, a tube and a ladder to change between the two levels, and were kept in groups of up to 12 female mice to allow manyfold social interactions (Figure 2—figure supplement 1A–C). Ctr mice were raised in pairs in standard cages without any equipment (Figure 2—figure supplement 1D, E). The mice were transduced with AAVs as described above and 3–6 weeks after transduction, they were anesthetized and a cranial window was implanted above the visual cortex. Imaging commenced about 2.5 hr after onset of the anesthesia. We acquired two-color z-stacks mainly of dendrites, which were in parallel to the focal plane. To analyze differences in morphology of the dendritic spines, we encircled the spine head and PSD95 at their largest extent and computed the respective area (Figure 2A, see Materials and methods section for details). We only considered spines containing a PSD95 label and therefore most likely form functional synapses. Occasionally we observed long, thin spines without a head, sometimes called filopodia in the literature, which were not analyzed. The spine head area correlated with the PSD95 area for EE and Ctr (Figure 2B), corroborating EM and two-photon microscopy studies (Arellano et al., 2007; Cane et al., 2014; Meyer et al., 2014). The histograms of spine head (Figure 2C) and PSD95 area (Figure 2D) were positively skewed and the distributions were significantly different between EE and Ctr mice. We determined a median spine head area for EE housed mice of 0.527 (interquartile range [IR]: 0.359–0.844) µm2 and 0.462 (IR: 0.290–0.787) µm2 for Ctr mice. The area of PSD95 on the same set of spine heads was 0.158 (IR: 0.107–0.269) µm2 in median for EE mice and 0.161 (IR: 0.092–0.286) µm2 for Ctr mice which is in accordance with previous EM studies reporting 0.15 µm2 for the PSD area in the mouse visual cortex (Harris and Weinberg, 2012). This size of the PSD95 area of layer 5 pyramidal neurons is slightly larger than our previously reported diameter of 354 nm which corresponds to ~0.10 µm2 for a circular distribution, obtained in a ubiquitously expressing PSD95-EGFP knock-in mouse (Wegner et al., 2018); therefore, the larger size of the PSD95 area could reflect the larger size of the spine heads of layer 5 pyramidal neurons (Konur et al., 2003). To further dissect these differences, we next plotted the histograms of the logarithm of spine head (Figure 2E) and PSD95 areas (Figure 2F). All four histograms (Figure 2E and F) are symmetric and well described by a Gaussian function. This is in line with prior work showing that the spine and PSD95 fluorescence is log-normally distributed in general, which is predicted by multiplicative processes of ongoing spine plasticity (Hazan and Ziv, 2020; Loewenstein et al., 2011). The Gaussian function fitting the spine head area is shifted to the right and is narrower for EE housed mice (Figure 2E). This is manifested by a significantly larger spine head area (Figure 2G) and smaller variance of the size distribution (Figure 2I) of EE housed mice, which might imply a preferential loss or adaptation of small spines (Figure 2E). The distributions of the logarithm of the PSD95 area (Figure 2F) are almost centered and thus the average area is not significantly different between EE and Ctr mice (Figure 2H). However, the variance (Figure 2J) is smaller for EE housed mice which is also visible in the Gaussian distribution that is narrower for EE housed mice (Figure 2F), that is, having less extreme values. Since previous studies often used the brightness of PSD95 assemblies or spines, respectively, as a measure of size, we also analyzed the PSD95 brightness and found a coefficient of determination R2 = 0.76 between the brightness and nanoscale size (Figure 2—figure supplement 2A), indicating that the brightness is a deficient correlate of synaptic size. We also analyzed the spine density which was not significantly different between EE and Ctr housed mice (Figure 2—figure supplement 2B). These results show that EE leaves a trace in spine head size and variability of the post-synaptic size, both of which have been shown to correlate with synaptic strength (Holler et al., 2021).

Figure 2 with 2 supplements see all
Size distributions of spine head and PSD95 area are sharper and show larger heads for mice housed in environmentally enriched (EE) than in standard (control [Ctr]) cages.

(A) Stimulated emission depletion (STED) images of dendritic spines (magenta) and associated PSD95 assemblies (green). Images are smoothed; maximum intensity projection (MIP) (left), contrast enhanced images for area analysis (middle, right). Spine heads (middle) and PSD95 assemblies (right) were encircled to compute the area. (B) Strong correlation of absolute spine head and PSD95 area in Ctr (orange) and EE (blue) housed mice. Linear regression lines are dashed and Pearson’s correlation coefficient r is displayed (EE and Ctr, deviation from zero: p < 0.0001). (C, D) Frequency distributions of spine head area (C) and PSD95 area (D) are positively skewed and significantly different between EE and Ctr housed mice (Kolmogorov-Smirnov test, C: ****p < 0.0001, D: *p = 0.013). Graphs display center of BIN, single large values are cut off. (E, F) Same data as shown in (C, D), but logarithmic values. Solid lines represent Gaussian functions fitted to the respective histogram. (G–J) Mean (G: EEspine: –0.259, Ctrspine: –0.317, H: EEPSD: –0.771, CtrPSD: –0.781) and variance (I, J) of logarithmic data shown in (E, F) + SEM (unpaired t-test with Welch’s correction: G: ****p < 0.0001, H: p = 0.54, I: **p = 0.006, J: **p = 0.003). Number of analyzed mice and spines: EE: 4x ♀-mice, nSpine/PSD95 = 795; Ctr: 4x ♀-mice, nSpine/PSD95 = 634.

Weak correlation of PSD95 and spine head area changes over minutes to hours

We showed above a strong correlation between the size of PSD95 assemblies and the spine head (Figure 2B) which corroborates previous findings of close structural correlation between PSD and spine size (Arellano et al., 2007). However, on which time scale the dynamic changes between these two features are linked in vivo has remained unknown. We thus asked whether and how temporal changes of these parameters were correlated. Therefore, we performed time-lapse STED microscopy of spine morphology and PSD95 for EE and Ctr housed mice for up to 4 hr as described above and analyzed temporal changes of these parameters (Figure 3A–L). Imaging commenced ~2.5 hr after onset of the anesthesia and the time-lapse was recorded at baseline, without a stimulation. We recorded STED images at different fields of view (FOV); each FOV was recorded for three time points at a time interval Δt of either 30 min (Figure 3A and B), 60 or 120 min. Over these time periods the spines were mostly stable. Occasionally, a spine was lost or a new one appeared; none of these dynamic spines carried PSD95 and they were therefore most likely highly dynamic filopodia (Berry and Nedivi, 2017). For each time point, the size of the spine head and PSD95 assembly was analyzed as described above. The average spine head area and PSD95 area did not show large changes over the time period of 120 min (Figure 3C). Thus, we did not observe directed changes as a result of light-induced stimulation or phototoxicity and neither observed blebbing. We computed normalized size changes over time so that both, positive (growth) and negative (shrinkage), changes were symmetric with a boundary value of ±1. Figure 3D–G shows scatter plots of the normalized changes of the spine head over changes of the PSD95 area for each time interval (Figure 3D–F) and cumulated changes of all time intervals (Figure 3G); the data points are scattered over all four quadrants (for percentage changes refer to Figure 3—figure supplement 1A–C). A linear regression analysis revealed a weak but significant positive correlation (r between 0.15 and 0.39, Figure 3D–G), such that an increase of PSD95 area is more frequently accompanied by an increase in spine head area and vice versa (Figure 3K). Some data points, however, also represented anti- or negatively correlated changes, that is, shrinking PSD95 areas on a growing spine or vice versa (Figure 3L). Interestingly, PSD95 assemblies displayed larger normalized changes in size than spine heads, which is evident from the elliptic distributions in Figure 3D–G or the median percent changes (Figure 3—figure supplement 1D, E); spine heads grew by ~20% and shrunk by ~15% while PSD95 assemblies grew by ~20–25% and shrunk by 25–30%. To further quantify the portion of correlated and anti-correlated changes, we performed a principal component analysis (PCA). Principal component 1 (PC1) is by definition the axis along which the data shows the maximum of variance and the eigenvalue of PC1 is the variance along this axis. The PC1 vector was for all three time intervals in the first (and third) quadrant (Figure 3D–F) and therefore attributing for positively correlated changes. The second principal component (PC2) is by definition perpendicular to PC1 and therefore in the second (and fourth) quadrant (Figure 3D–F), attributing for negatively correlated changes. The variance of PC1 and thus correlated changes is ~3 times larger at all time intervals than that of PC2, that is, anti-correlated changes (Figure 3H, I; Figure 3—figure supplement 1F). Thus, we found besides the positively correlated changes also a large portion of anti-correlated changes reflecting a temporal uncoupling of spine head and PSD95 morphology. For all time intervals, the variance of PC1 and PC2 was higher for Ctr housed mice, although not always significant (Figure 3H, I). To compare Ctr versus EE housed mice, we averaged the variance over all time intervals (Figure 3J) as the changes did not vary significantly within one group. Thus, the variance of Ctr housed mice was statistically significantly higher for PC2 but not for PC1. This indicates that negatively correlated changes contribute much less in the EE housed mice indicating a stronger, positive coupling between changes in spine head size and PSD95 area.

Figure 3 with 1 supplement see all
Temporal changes of spine head and PSD95 area are weakly positively correlated with a larger variability for control (Ctr) than environmental enrichment (EE) housed mice.

(A, B) Representative sections of spine heads and corresponding PSD95 of time-lapse in vivo two-color stimulated emission depletion (STED) microscopy images for EE (A) and Ctr (B) housed mice at time points of 0, 30, and 60 min. Images are smoothed and shown as maximum intensity projection (MIP). (C) Median ±95% confidence interval (CI) of spine head and PSD95 areas of Ctr (triangle, orange) and EE (square, blue) housed mice over time. (D–G) Normalized changes in spine head and PSD95 area after 30 min (D), 60 min (E), and 120 min (F) time intervals and compiled changes of all time intervals (G). Linear regression lines are dashed (deviation from zero: p < 0.0001) and Pearson’s correlation coefficient r is displayed. Solid lines represent the principal components 1 and 2 (PC1, PC2) of the principal component analysis (PCA) for Ctr (red) and EE (blue), respectively. (H–J) Variance along PC1 and PC2 of normalized changes plotted in (D–G); variance of PC1 (H), PC2 (I), and compiled variance over all time intervals (J) + SEM (unpaired t-test EE vs. Ctr; H: 30 min: p = 0.36, 60 min: *p = 0.045, 120 min: p = 0.40; I: 30 min: *p = 0.027, 60 min: ***p < 0.001, 120 min: p = 0.17; J: PC1: p = 0.063, PC2: ****p < 0.0001). (K, L) Illustration of temporal changes between spine head size and PSD95 area; positive correlation (K): growth and shrinkage of spine head size and area of PSD95 assemblies goes hand-in-hand; negative correlation (L): a growing PSD95 assembly on a shrinking spine and vice versa (K). (C–J) Number of analyzed mice and spines: 4x ♀-mice for EE and 4x ♀-mice for Ctr; number of spines with PSD95 assemblies: EE, t = 0: nSpine/PSD95 = 795, t = 30 min: 326, t = 60 min: 388, t = 120 min: 151; Ctr, t = 0: 634, t = 30 min: 233, t = 60 min: 285, t = 120 min: 189. Time intervals are pooled; for example, Δt = 30 min includes 0–30 and 30–60 min.

Figure 3—source data 1

Spine head and PSD95 assembly sizes for each time point.

https://cdn.elifesciences.org/articles/73603/elife-73603-fig3-data1-v2.xlsx

In summary, the synaptic structure is highly dynamic and spine heads and PSD95 assemblies change in size by >20% already after 30 min. The variance of these changes is smaller and positively correlated changes are more pronounced for EE housed mice. These results suggest that EE housed mice undergo smaller but more directed morphological changes under anesthesia.

Multiplicative downscaling of PSD95 area is different between EE and Ctr housed mice

Since our parameters were log-normally distributed, indicating a multiplicative dynamic, we asked whether the temporal changes were also regulated by multiplicative processes (Hazan and Ziv, 2020; Loewenstein et al., 2011). Thus, we plotted the synaptic size after different time intervals Δt as a function of the original size (Figure 4A–F). A linear regression analysis revealed a slope <1 and positive y-intercept for spine head and PSD95 area, for both EE and Ctr housed mice. Plotting the size difference between the different time points as a function of the original size indicates that large spines tend to shrink and small spines tend to grow; this becomes evident as the changes are rather negative for larger sizes and positive for smaller sizes (Figure 4—figure supplement 1). Such a tendency is often called regression to the mean and is a frequently observed statistical phenomenon. However, it is driven by biological processes, and the strength of those changes may vary under different conditions such as between EE and Ctr. To quantify these changes in synapse and spine head size, we use a Kesten process which was recently applied as a model for spine dynamics and their skewed size distribution. In this model a noisy multiplicative downscaling is combined with a noisy additive term (Hazan and Ziv, 2020; Statman et al., 2014). Thus, the negative slope as shown in Figure 4—figure supplement 1 which is equal to 1-slope of Figure 4 would be regarded as a time-dependent multiplicative downscaling factor. We observed a significantly different slope, that is, multiplicative downscaling for the PSD95 area between EE and Ctr housed mice (Figure 4F). While the slope was constant or decreased slightly over time for EE housed mice, it rose up to nearly one for Ctr mice. Interestingly, this effect was specific for PSD95 and was not observed for the spine head size (Figure 4A–C); the slope for spine head size changes was rather constant over time and similar between EE and Ctr mice. In summary, we found a significant increase in downscaling over time for PSD95 in EE housed mice as compared to Ctr after 60 and 120 min.

Figure 4 with 1 supplement see all
Environmental enrichment (EE) housed mice show an increase in multiplicative downscaling of PSD95 area over control (Ctr).

(A–F) Spine head and PSD95 area after different time intervals Δt of 30, 60, and 120 min as function of their initial area at time t (A, B, D, E). Solid lines show linear regression fits of the displayed equation; the identity line is dashed (analysis of covariance; are slopes equal? A: p = 0.53, B: p = 0.29, D: ****p < 0.0001, E: p = 0.39). (C) Slope ± SE of fit to spine head changes (A, B) (left, are slopes different? EE vs. Ctr: 30 min: p = 0.37, 60 min: p = 0.11, 120 min: p = 0.26) and Pearson’s correlation r (right) of linear regression. (F) Slope ± SE of fit to PSD95 area changes (D, E) (left, are slopes different? EE vs. Ctr: 30 min: **p < 0.01, 60 min: ****p < 0.0001, 120 min: ****p < 0.0001) and r value (right). (G) Cross-correlation between spine head and PSD95 area for EE and Ctr housed mice. Error bars are bootstrap ± SD. Same data set as in Figure 3; the same time intervals Δt are pooled.

Despite fluctuations, spine heads and PSD95 assemblies are stable on average over 2 hr

Next, we asked how these large temporal changes influence the stability of the synapse. Do growing spines continue to grow and do shrinking spines continue to shrink? Thus, we computed the Pearson’s correlation coefficient r between the different time points (Figure 4C and F, right). The r value was relatively constant over all time intervals and not significantly different between spine heads and PSD95 assemblies or EE and Ctr house mice. As such we did not find changes in r or its corresponding coefficient of determination R2 value such as described in vitro (Hazan and Ziv, 2020) at our time window of up to 2 hr. This indicates that large spine heads tend to stay rather large and smaller ones stay small.

No temporal shift between spine head and PSD95 area changes at baseline

We found a strong correlation coefficient of ~0.8 between spine head and PSD95 area (Figure 2B). However, if the PSD expands with a temporal delay of ~1 hr to the spine head as suggested by the work of Bosch et al., 2014; Meyer et al., 2014, the correlation should be even higher when comparing spine heads and PSD95 area at different time points. Therefore, we computed the cross-correlation between these measures for all time intervals. Figure 4G shows that the cross-correlation between the PSD95 and spine head area was ~0.8 over all time intervals of 0–120 min and thus we did not observe directional changes of these parameters at baseline. In particular, we did not observe that PSD95 increase would systematically succeed a spine head increase or vice versa as both would result in an asymmetric cross-correlation.

PSD95 nanoorganization changes faster in EE mice

Previous EM studies have shown that PSDs on large spines, often called mushroom spines, are frequently perforated (Stewart et al., 2005). In our previous publication (Wegner et al., 2018), we investigated PSD95 morphology in homozygous knock-in mice and showed that PSD95 assemblies on large spines were often perforated and appeared ring-like or clustered. This nanoorganization was highly dynamic and changed in shape within a few hours. Now, we asked whether these changes would be different for mice housed in EE. Figure 5A shows examples of two-color STED images of perforated PSD95 assemblies and their associated spine revealing temporal changes in the PSD95 nanopattern. The nanopattern was similar to that of the knock-in mice (Wegner et al., 2018) and is quite complex; we often observed clusters but also continuous structures of horseshoe or more complex shapes. Previous studies have counted number of nanodomains for these structures (Hruska et al., 2018). However, in our opinion, this does not satisfactorily reflect the complexity of the structure and thus we performed a visual inspection analogous to Wegner et al., 2018. Morphological changes were categorized for each time point as no change, subtle change, or strong change with respect to the first observation at t = 0 by three independent persons (Figure 5B and C). Ctr housed mice showed a clear trend: the longer the measurement interval, the greater the morphological change (Figure 5C); the percentage of PSD95 nanoorganization showing no change decreased from ~40% to ~10%, while the percentage for strong changes nearly tripled for Ctr. For EE housed mice, we observed similar, strong changes within all investigated time intervals; ~20% of the PSD95 assemblies did not change at all and ~80% underwent a change at all time points (Figure 5B). Although the percentage changes with respect to t = 0 were similar for different time intervals, it should be noted that this does not exclude dynamic changes between the time points. Thus, changes in Ctr housed mice increased over the observation period and reached a level similar to that of EE housed mice after 120 min. This suggests that the PSD95 nanoorganization is more dynamic in EE mice than in mice raised under control conditions within the investigated time course. Now, we asked whether the different dynamic in morphological changes would be reflected as well in a different nanoorganization. Therefore, we categorized the nanoorganizations into perforated (a continuous shape with a sub-structure such as ring- or horseshoe-like) or clustered. Fewer nanoorganizations of EE housed mice were perforated and a larger fraction showed three or more clusters compared to Ctr mice (Figure 5D). This suggests that the PSD95 nanoorganization might play a substantial role in synaptic remodeling and is specifically shaped by experience or activity.

PSD95 nanopattern is different between environmental enrichment (EE) and control (Ctr) and changes faster for EE housed mice.

(A) Sections of two-color stimulated emission depletion (STED) images (smoothed, maximum intensity projection [MIP]) of EE and Ctr housed mice at the indicated time points showing the spine membrane (magenta) and PSD95 (green). Morphological change is indicated with: 0 = no change; + = subtle change; ++ = strong change. (B, C) Stacked histogram of the relative frequency of morphological changes in PSD95 nanopattern in EE (B) and Ctr (C) housed mice; all changes refer to t = 0 min. (D) Morphometry of PSD95 nanopattern of EE and Ctr housed mice at t = 0. (B–D) Number of analyzed PSD95 assemblies in EE: 30 min: n = 38, 60 min: n = 68, 120 min: n = 29; Ctr: 30 min: n = 27, 60 min: n = 48, 120 min: n = 38.

Figure 5—source data 1

Images of all analyzed perforated PSD95 assemblies.

https://cdn.elifesciences.org/articles/73603/elife-73603-fig5-data1-v2.pdf

Discussion

Interactions with the environment play a key role in restructuring and refining the neuronal circuitry in the brain. We here used time-lapse in vivo STED microscopy, a superresolution light microscopy technique with nanoscale resolution, to study experience-dependent changes of the synaptic nanoorganization as well as its nanoplasticity in the intact brain. We designed a two-color, virtually crosstalk-free in vivo STED microscope with a resolution of 70–80 nm for simultaneous imaging of spine morphology and PSD95 in the visual cortex of anesthetized mice. We found a significantly smaller variability in size of the spine head and PSD95 nanoorganization in EE mice while the average spine head size was increased compared to Ctr mice. Both parameters were highly volatile and we observed an average growth of >20% and shrinkage of >18% for spine heads and PSD95 assemblies within 30 min while percent changes in PSD95 assembly size were slightly larger than those of the spine head. About 3/4 of these changes were positively correlated and ~1/4 showed a negative correlation; EE housed mice exhibited a smaller variance of changes than Ctr mice, and less negatively correlated changes. All parameters exhibited multiplicative downscaling which was significantly different for PSD95 assembly size between EE and Ctr mice. Dynamical rearrangement of the nanostructure was faster in EE housed mice.

Two-color in vivo STED nanoscopy

Previous studies mainly used two-photon microscopy to study synapse and spine plasticity in vivo and in vitro (Bosch et al., 2014; Cane et al., 2014; Meyer et al., 2014; Villa et al., 2016). Due to its limited optical resolution of 300500 nm, spine brightness is often taken as a measure of size (Cane et al., 2014; Hofer et al., 2009; Matsuzaki et al., 2004). This is not very accurate since the brightness can be influenced by many factors such as expression level of the fluorescence label, the excitation laser intensity, group velocity dispersion, or scattering in the tissue. Therefore, spine brightness is typically normalized to the brightness of the dendrite, making comparative studies and determination of absolute sizes difficult. To circumvent the diffraction limit, two-photon microscopy was combined with STED to achieve nanoscale resolution (Moneron and Hell, 2009; Panatier et al., 2014). However, a drawback of this approach is the relatively high crosstalk when using two colors. Thus, the most commonly used EGFP and EYFP have a crosstalk of as much as 92% and 27% in the respective other channel, requiring linear unmixing (Tønnesen et al., 2011). Unmixing works well for structures which are relatively bright and spatially separated such as for volume labeled spines and astrocytes (Panatier et al., 2014) but can produce artifacts in dark and overlapping structures such as the post-synapse and spine we were studying in this project. Therefore we used an approach from two-color STED with organic dyes in the red emission spectrum (Bottanelli et al., 2016; Göttfert et al., 2013) and designed a two-color STED microscope with two different excitation lasers (one-photon excitation) and two spectrally separated detection channels. This approach gave us an almost negligible crosstalk of 5% for EGFP and 8% for Citrine rendering unmixing obsolete.

High volatility of spine heads and PSD95 assemblies

We used the transcriptionally regulated antibody-like PSD95.FingR, which efficiently labels endogenous PSD95 (Cook et al., 2019; Gross et al., 2013) and excludes artifacts that occur after PSD95 overexpression (El-Husseini et al., 2000; Stein et al., 2003).

Spine heads and PSD95 assemblies were highly volatile already at 30 min intervals. The spine head growth of ~20–30% on average and ~200% maximum (Figure 3—figure supplement 1) is very similar to the spine head changes we have reported recently in the cortex of a transgenic mouse over 3–4 days (Steffens et al., 2021) and to spine head increase following chemical LTP induction (Kopec et al., 2006; Otmakhov et al., 2004) or glutamate uncaging (Meyer et al., 2014); thus these changes might reflect potentiation of single spines. The percentage increase in amount of PSD95 after glutamate uncaging reported before, however, was always smaller than the spine volume increase (Bosch et al., 2014; Meyer et al., 2014); it is therefore interesting that we observe a slightly larger percentage increase of PSD95 assembly area than spine head area at baseline in Ctr and EE housed mice. However, it was shown that MMF anesthesia reduces spiking activity and mildly increases spine turnover in the hippocampus (Yang et al., 2021). Thus, the plasticity of spine heads and PSD95 assemblies might be different in the awake state and under intense processing of visual information.

Temporal changes of PSD95 assemblies and spine heads are largely uncorrelated

While we observed a substantial correlation between the absolute spine head and PSD95 assembly size (r ~0.8, Figure 2B), corroborating previous EM studies (Arellano et al., 2007; Harris et al., 1992), we found only weak correlations between their temporal changes (r ~0.2–0.4, Figure 3D–G). This supports in vitro LTP experiments which have shown a temporal delay of ~60 min between spine and PSD95 increase (Bosch et al., 2014; Meyer et al., 2014). Although these experiments were performed in cell culture and with PSD95 overexpression, they indicate that spine and PSD enlargement might be regulated by different pathways (Compans et al., 2016; Herring and Nicoll, 2016). However, we found no directional changes that would demonstrate that the PSD95 size systematically follows an increase in spine head size since the cross-correlation is largely symmetric (Figure 4G). This could be due to the fact that we only studied baseline changes, without a dedicated stimulation protocol that synchronizes and amplifies the temporal changes.

But how can the time course of AMPA receptor incorporation and PSD95 increase be so different although AMPA receptors are linked to PSD95 by TARPs? A slot model proposes that the actin polymerization during LTP increases the number of slots which can harbor AMPA receptors (Herring and Nicoll, 2016). Thus, the increase of AMPA receptors would not depend on an increase of the amount of PSD95 proteins at the synapse but on an activation of binding sites of AMPA receptor to the PSD95 by a still unknown mechanism.

Synapse and spine sizes of EE housed mice are sharper defined and change concordantly

To study whether different activity conditions during rearing influence synapse and spine size and plasticity, we compared adult mice housed in standard cages with age matched EE housed mice and analyzed synaptic morphology and plasticity in the visual cortex after the critical period. We found a significantly smaller variance and thus narrower distribution of spine head and PSD95 area in EE housed mice. At the moment different models of how neuronal networks adapt to changes in activity to undergo homeostatic plasticity are considered (Lee and Kirkwood, 2019). In the model of synaptic scaling, an increase in activity leads to a downscaling of excitatory synapses and vice versa. The downscaling is multiplicative and therefore changes affect the average as well as broadening of the size distribution as shown, for example, in a silenced network (Hazan and Ziv, 2020). We did not observe such a stringent synaptic scaling although the variance shows the same tendency; a decrease in PSD95 and spine size variability after enhanced activity of EE housed mice are opposed, and therefore in line with the broadening of the size distribution in silenced networks (Hazan and Ziv, 2020) or after deprivation in vivo (Keck et al., 2013). This may be supported by recent in vivo observations that in the adult cortex homeostatic plasticity is rather input-specific and not multiplicative for the whole ensemble of synapses (Barnes et al., 2017). A sole decrease in variability was described before for the neuronal firing rates after stimulation. This was observed in different brain regions and even when the change in mean firing rate was little (Churchland et al., 2010). The authors of this study concluded that the variance decline of the firing rate implies that cortical circuits become more stable. The same might apply to the spines and synapses of EE housed mice for which we observe less extreme values. This may suggest that the synapses are better defined by training in the enriched environment and thus the neural network is more stable. Interestingly, we also observed a tighter correlation between spine head and PSD95 changes for EE housed mice since negatively correlated changes are significantly reduced. As discussed above, however, the dynamic might be influenced by the anesthesia and different in the awake state.

Stronger multiplicative downscaling in EE housed mice for PSD95

We found that changes of spine heads and PSD95 assemblies correlate with their absolute size for EE and Ctr housed mice; small spines tended to grow and large spines tended to shrink (Figure 4A–F). The slope of the linear regression line for such size changes, as plotted in Figure 4A–F, can be viewed as a time-dependent multiplicative downscaling factor of a Kesten process (Ziv and Brenner, 2018). Statistical models such as the Kesten process offer an explanation on how to link synaptic size fluctuations with the shape and scaling of the synaptic size distribution. For example, it was shown that silencing a neuronal network and thus reducing its activity not only resulted in an average size increase and broader distribution but also in a weaker multiplicative downscaling (Hazan and Ziv, 2020). Our observations on PSD95 area changes are in line with these results in such that the increase in activity by enrichment strengthened the multiplicative downscaling over time for PSD95 after 60 and 120 min (Figure 4F). Therefore, we find the same tendency in vivo as the in vitro silencing experiment. However, it should be noted that our in vivo measurements were performed under anesthesia and directly after implanting a cranial window; therefore differences are due to the different rearing conditions and not to changes in activity at the moment of the measurement. The in vitro silencing, in contrast, continued over the measurements. The increase in multiplicative downscaling we observe might explain the narrower size distribution of the PSD95 area according to the measurements and simulations by Hazan and Ziv, 2020. However, a downscaling factor of 0.7–0.8 which we found for PSD95 in EE housed mice (Figure 4F) was observed in vitro by Hazan and Ziv only after ~20 hr. And, this does not explain the decrease in multiplicative downscaling we observed for Ctr housed mice from 30 to 120 min intervals. Moreover, we did not find changes in the Pearsons’s correlation coefficient r for the different time intervals such as described by Hazan and Ziv, 2020. A refinement of the model and additional measurement will certainly be needed in the future. We note in passing that it should not be surprising that our in vivo experiments do not completely match expectations from artificially silenced neuronal culture experiments. And, as discussed above, there is evidence that homeostatic plasticity is not global but input-specific (Barnes et al., 2017).

Enhanced plasticity of the nanopattern by experience

With our superresolution technique we often observed a perforated nanoorganization of PSD95 undetectable by two-photon or conventional imaging (Figure 5). The pattern of the PSD95 nanoorganization labeled with PSD95.FingR was very similar to the structures we have observed earlier in a PSD95-EGFP knock-in mouse with in vivo STED microscopy (Wegner et al., 2018). And, this nanopattern is also similar to perforated PSDs reported by electron microscopy (Arellano et al., 2007; Harris and Weinberg, 2012; Toni et al., 2001). It is very difficult to analyze this pattern quantitatively because of its great diversity. As long as the functional consequences are not clear, it is very difficult to design an appropriate analysis routine; for example, should we quantify the area covered with PSD95 only and which role does the distance between clusters or diameter of a ring play? Since we could not apply stringent and justifiable rules for a quantitative analysis, we decided to perform only a visual inspection of the shape, but include all analyzed images into the supplement for transparency. In this way, we found that the nanopattern was different between EE and Ctr and that its structural changes occurred more rapidly in EE housed mice, suggesting greater synaptic flexibility. The functional consequences of this nanopattern, however, are not fully understood. An emerging view is that clusters of pre- and post-synaptic proteins are trans-synaptically aligned in ‘nanocolumns’ which are organized by activity (Chen et al., 2018). Moreover, computational simulations predict that changes in the shape of the PSD are a way to align post-synaptic receptors to pre-synaptic release sites (Franks et al., 2003; Savtchenko and Rusakov, 2014). This implies that a reorganization of receptors might alter synaptic strength – independently of changes in the amount of receptors (Chen et al., 2018). Our finding of an increased structural plasticity of the PSD95 nanoorganization but not increased average size in EE mice might therefore reflect the reported increase of synaptic plasticity after enrichment (Artola et al., 2006; Buschler and Manahan-Vaughan, 2012).

Outlook

Our results demonstrate that experience influences the synaptic nanopattern and facilitates structural remodeling of the synaptic nanoorganization. Two-color STED microscopy opens novel avenues for the in vivo investigation of the synaptic nanoorganization and its dynamics in the living brain. In the future this approach could, for example, be extended to study the plasticity of nanocolumns after experience, as well as over longer time intervals in chronic experiments (Steffens et al., 2021) and different brain regions.

Materials and methods

DNA constructs

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A detailed description of the different cloning steps to obtain pAAV-ZFN-hSyn-DIO-PSD95.FingR-Citrine-CCR5TC, with a DIO for the expression of the transcriptionally regulated antibody-like protein PSD95.FingR, can be found in Willig et al., 2021.

The plasmid pAAV-hSyn-DIO-myr-EGFP-LDLR(Ct) for dendritic membrane labeling was cloned as follows: First, we PCR amplified a myristoylation (myr) site-attached EGFP including the myristoylation sequence ATGGGCTGTGTGCAATGTAAGGATAAAGAAGCAACAAAACTGACG in the forward primer. Second, we split the C-terminal (Ct) cytoplasmic domains of LDLR (GenBank: AF425607, amino acid residues 813–862) (Kameda et al., 2008), finally designated as LDLR(Ct), into two parts (part 1 and part 2), designed 5′ phosphorylated forward and reverse primers for each part and hybridized each pair (Table 1). In the third and final step, the endonuclease-digested myr-EGFP PCR together with the LDLR(Ct) part 1 and LDLR(Ct) part 2 were ligated into plasmid pAAV-hSyn-DIO-EYFP digested with AscI and NheI to finally obtain pAAV-hSyn-DIO-myrEGFP-LDLR(Ct).

Table 1
Overview of the primers and endonucleases.

a: produced by PCR, b: generated by hybridization, P: phosphorylated; underlined nucleotides: restriction sites or part of them.

Target constructPrimerRestriction sitesDNA-insert
pAAV-hSyn-DIO-myrEGFP-LDLR(Ct)5´- agttatgctagcatgggctgtgtgcaatgtaaggataaag aagcaacaaaactgacgatggtgagcaagggcgaggag –3´NheIMyristol (myr)-EGFPa
5´- cgcaccggtcttgtacagctcgtccatg-3´AgeI
P-5´-ccggtcggaactggcgcctgaagaatatcaacagc atcaatttcgataaccccgtgtaccagaagaccacagaggat –3´AgeILDLR(Ct)-part1b
P-5´-cagctcatcctctgtggtcttctggtacacggggttatcgaaa ttgatgctgttgatattcttcaggcgccagttccga –3´AgeILDLR(Ct)-part1b
P-5´-gagctgcacatttgcaggtcccaagacgggtacacctatcc aagtcggcagatggtcagcctcgaggacgatgtggcctgagg –3´AscILDLR(Ct)-part2b
P-5´- cgcgcctcaggccacatcgtcctcgaggctgaccatctgcc gacttggataggtgtacccgtcttgggacctgcaaatgtg –3´AscILDLR(Ct)-part2b

Generation of the Cre recombinase expression plasmid pAAV-hSyn-Cre was performed as described in Wegner et al., 2017.

The crosstalk was determined by expression of a fusion protein consisting of the small peptide Lifeact (LA), which directly binds to F-actin, and a fluorescent protein: pAAV-hSyn-LA-EYFP and pAAV-hSyn-LA-EGFP (Willig et al., 2021; Willig et al., 2014).

Virus production

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Recombinant AAV particles with mixed serotypes 1 and 2 of the pAAV plasmids encoding the proteins of interest were produced in HEK293-FT cells. The entire procedure is described in detail in Wegner et al., 2017 and is applied here with the following modifications: After DNaseI treatment (30 min at 37°C), the suspension was centrifuged at 1200 g for 10 min. The supernatant was filtered through a 0.45 µm sterile filter (Merck/Millipore, Darmstadt, Germany) and first applied to an Amicon Ultra-15, MWCO 100 kDa, centrifugal filter unit (Merck/Millipore, Darmstadt, Germany), followed by a Vivaspin 500, MWCO 100 kDa, centrifugal concentrator (Sartorius, Göttingen, Germany) to wash the virus in Opti-MEM Medium (ThermoFisher Scientific, Darmstadt, Germany) and concentrate to a final volume of 150 µl.

Animals

All animal experiments were performed with C57BL/6J female mice reared at the animal facility of the Max Planck Institute for Multidisciplinary Sciences, City Campus, in Göttingen and housed with a 12 hr light/dark cycle, with food and water available ad libitum. Experiments were performed according to the guidelines of the national law regarding animal protection procedures and were approved by the responsible authorities, the Niedersächsisches Landesamt für Verbraucherschutz (LAVES, identification number 33.9-42502-04-14/1463). All efforts were made to avoid animal suffering and to minimize the number of animals used.

Housing conditions

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EE animals were born and raised in the commercially available Marlau cage (Marlau, Viewpoint, Lyon, France) (Fares et al., 2012), 580 × 400 × 320 mm3 in size including two floors, providing an extensive exploration area (Figure 2—figure supplement 1). Three pregnant female mice were placed in an EE cage about 1 week before delivery. Pups were weaned and split by sex at post-natal day 30. The ground floor of the EE cage consisted of two separate compartments: the smaller part contained food and the larger part contained water, running wheels, and a red house. To get food, the mice had to use a stairway to reach the second floor where a maze was placed. After passing the maze, the mice slide through a tunnel back to the ground floor, directly into the smaller compartment and had free access to food. By passing a one-way door, they could enter the larger part of the ground floor to get water. To increase novelty and maintaining cognitive stimulation, the maze was changed three times per week with a total of 12 different configurations. Ctr mice were born and raised in standard cages of 365 × 207 × 140 mm3 size. They were kept in two to three animals per cage, which were solely equipped with nesting material.

AAV transduction

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Adult (>12 weeks) female C57BL/6J mice were stereotaxically transduced with a mixture of AAV with mixed serotypes 1 and 2. AAV1/2-ZFN-hSyn-DIO-PSD95.FingR-Citrine-CCR5TC, AAV1/2-hSyn-Cre, and AAV1/2-hSyn-DIO-myrEGFP-LDLR(Ct) were stereotaxically injected at the same time into the visual cortex of the left hemisphere at a depth of ~500 µm below the pia transducing mainly layer 5 pyramidal neurons by using the following coordinates: 3.4 mm posterior to the bregma, 2.2 mm lateral to the midline, at an angle of 70° to the vertical, and with a feed forward of ~750 µm. For that purpose mice were anesthetized by intraperitoneal injection of 0.05 mg/kg fentanyl, 5 mg/kg midazolam, and 0.5 mg/kg medetomidin (MMF). The mouse was head fixed with a stereotaxic frame and placed on a heating pad throughout the whole procedure to maintain constant body temperature. The depth of the anesthesia was controlled by monitoring the pulse rate and O2 saturation with a pulse oximeter at the thigh of the mouse and body temperature measured with a rectal temperature probe. A gas mixture with high O2 (47.5 vol%) and CO2 (2.5 vol%) content was applied to the mouse’s nose, significantly increasing the oxygenation. The eyes of the mouse were covered with eye ointment and the head was disinfected with 70% ethanol. The skin was cut by an ~0.5 cm long incision and a drop of local anesthetic (0.2 mg mepivacaine) was applied. Then, a 0.5 mm hole was drilled into the skull. 150 nl of AAV containing solution was pressure injected through a glass micropipette attached to a microinjector (Picospritzer III, Parker Hannifin Corp, Cleveland, OH) with an injection rate of ~50 nl/min. After injection, the pipette was kept at the target location for additional 2 min to allow the virus to disperse away. After retracting the micropipette, the incision was closed with a suture. Anesthesia was then antagonized by intraperitoneal administration of 0.1 mg/kg buprenorphine and 2.5 mg/kg atipamezole. The mouse was kept in a separate cage until full recovery and then put back into its original cage, and group-housed in the animal facility until the final experiment. All mice, EE and Ctr housed, were transduced with the same batch of purified virus, that is, the same virus concentration.

Craniotomy for in vivo STED microscopy

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Three to six weeks after viral transduction, a craniotomy was performed as described previously (Steffens et al., 2020). In brief, the mouse was anesthetized with MMF, placed on a heating pad and vital functions and depth of anesthesia were controlled throughout the final experiment as described above. The head was mounted in a stereotaxic frame. The scalp was removed and a flat head bar was glued to the right hemisphere to leave enough space for the cranial window above the visual cortex, rostral to the former viral injection site. After drilling a circular groove (~2–3 mm) into the skull, the bony plate was carefully removed without causing a trauma. The remaining dura and arachnoid mater were carefully removed with a fine forceps. A small tube was positioned at the edge of the opening to drain excess cerebrospinal fluid if necessary. The craniotomy was sealed with a 6 mm diameter cover glass glued to the skull. The mouse was mounted on a tiltable plate which can be aligned perpendicular to the optical axis of the microscope in a quick and easy routine before being placed under the microscope (Steffens et al., 2020).

Culture of primary hippocampal neurons, transduction, and imaging

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Primary cultures of rat hippocampal neurons were prepared from P0-P1 Wistar rats (RjHan:WI; The Jackson Laboratory, Bar Harbor, ME) of both sexes according to D’Este et al., 2017. Neurons were cultured at 37°C in a humidified atmosphere with 5% CO2 and transduced between 8 and 10 days in vitro with the respective AAVs. After an incubation time of 7 days, live-cell STED imaging was performed at room temperature.

In vivo two-color STED microscope

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A previously described custom-designed STED microscope (Wegner et al., 2018; Willig et al., 2014) was modified to accommodate virtually crosstalk-free two-color imaging as follows (Figure 1). Excitation light (Exc1) provided by a pulsed laser diode emitting blue light at 483 nm (PiLas, Advanced Laser Diode Systems, Berlin, Germany) was complemented by a second excitation beam (Exc2). The beam of a Ti:Sapphire laser (MaiTai; Spectra-Physics, Santa Clara, CA) was split into two, pumping an optical parametric oscillator (OPO; APE, Berlin, Germany) emitting 80 MHz pulses at 595 nm for STED and a supercontinuum device (FemtoWHITE800, NKT photonics, Birkerød, Denmark) generating white light. The white light was spectrally filtered for green light with a bandpass filter (BP1, BrightLine HC 520/5, Semrock, IDEX Health & Science, Rochester, NY) for selective excitation of EYFP or Citrine at 520 nm (Exc2, Figure 1A and B). Exc1, Exc2 and the STED beam were co-aligned with dichroic mirrors. After passing a scanning device (Yanus, Till Photonics-FEI, Gräfelfing, Germany) consisting of two galvanometric mirrors for x-y-scanning and relay optics, the three beams were focused by a 1.3 numerical aperture objective lens (PL APO, 63×, glycerol; Leica, Wetzlar, Germany). Additionally, the STED beam was passing a vortex phase plate (VPP; RPC Photonics, Rochester, NY) to create a doughnut-shaped focal intensity pattern featuring zero intensity in the center. Temporal overlap of all three pulsed laser beams was achieved electronically by synchronizing the blue laser diode, Exc1, to the Ti:Sapphire laser and optically by an optical delay line for Exc2. Z-scanning was performed by moving the objective with a piezo (MIPOS 100PL; piezosystem jena GmbH, Jena, Germany). The back-projected fluorescence light was split at 515 nm with a dichroic mirror (DM2, ZT502rdc-UF3; Chroma Technology Corporation, Bellow Falls, VT) into two beams. The shorter wavelength was reflected, filtered by a bandpass filter (BP3, BrightLine HC 504/12; Semrock) and focused onto a multimode fiber for confocal detection connected to an avalanche photodiode (APD, Excelitas, Waltham, MA). The transmitted, longer wavelength fluorescence light was also filtered with a bandpass filter (BP2, H544/23, AHF analysentechnik, Tübingen, Germany) and detected with an APD, respectively.

In vivo two-color STED imaging of anesthetized mice

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The in vivo STED microscopy was performed in layer 1 of the visual cortex at a depth of 5–20 μm below the pia. Spherical aberrations due to the tissue penetration were corrected on the first order by adapting the correction collar of the glycerol immersion objective for the best image quality at each FOV. Both excitation colors were alternated line-by-line; that is, a line was recorded by excitation with blue light (Exc1) and then the same line was recorded again with green excitation light (Exc2). Potential drift between images and movement of the spine or PSD95 was therefore negligible. For imaging we picked different FOV with dendrites parallel to the focal plane. After recording a two-color STED image stack, the position of the motorized micrometer stage (MS-2000, Applied Scientific Instruments, Eugene, OR) was noted and an overview image taken. This process was repeated several times for different positions. After 30 min the micrometer stage was moved back to the first position. The position was confirmed by recording coarse overview images of the dendrite and thereby adjusting the z-position to the right depth. STED images were recorded as z-stacks of ~20–40 µm in x and y with 500 nm axial steps at different positions of the cranial window. All positions were repeatedly imaged 3–4 times at intervals of 30 min to 2 hr. Therefore, some dendritic regions were investigated at the time points 0, 30, and 60 min, while other dendritic region within the cranial window was examined at the time points 0, 60, and 120 min or even at 0, 120, and 240 min. The benefit of a membrane label over the often used volume label is that the brightness of the spines and dendrites is similar whereas with a volume label, spine heads, and dendrites often outshine the small spine neck. Thus, either the much darker neck is not visible between the bright head and dendrite or the detector is saturated at the bright heads and dendrites.

All images were recorded with pixel dwell time of 5 µs, pixel size of 30 × 30 nm2 in x and y, and z-stacks of 500 nm step size. Blue and green excitation power was 4.5 µW, respectively, in the back aperture of the objective. The average STED power in the back aperture was 37–45 mW for static images and 15 mW for time-lapse imaging. EE and Ctr housed mice were imaged in random order but not blindly. Dendrites were randomly picked.

Data processing and analysis

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Confocal and STED images were acquired by the software Imspector (Abberior Instruments, Göttingen, Germany). Size, shape, and brightness analysis was performed manually in Fiji and blind with respect to the housing conditions (Schindelin et al., 2012). For spine head and PSD95 analysis, the first and last planes were omitted to ensure that the spine (or PSD95, respectively) was located completely in the focal plane. We only analyzed spines that also bore a PSD and only spines that extended from the dendrite mainly parallel to the focal plane. PSDs directly on the dendrite, most likely representing shaft synapses, and spines pointing upward or downward that could not be clearly resolved were not analyzed. First, images of each channel were processed as follows (Fiji commands shown in capital letters): (1) Smoothing: PROCESS> SMOOTH twice. (2) Brightness adjustment: IMAGE> ADJUST > BRIGHTNESS/CONTRAST set minimum to 1 instead of zero. (3) Overlay both one-color images: IMAGE> COLOR > MERGE CHANNELS. (4) Open ROI manager: ANALYSE > TOOLS > ROI MANAGER. Spine heads and PSD95 assemblies were encircled at their largest extent as shown in Figure 2A to compute their area. Perforations or cluster of PSD95 were encircled to include only PSD95. For the analysis of brightness, PSD95 images were processed as described above. Using the ELLIPTICAL SELECTION tool, each PSD95 assembly was encircled and the brightness was displayed by the variable ‘RawIntDen’, which is the sum of the intensity values of all pixels in the selected area. Spine density was obtained by dividing the total number of spines minus one by the length of the parent dendrite between the first and last spine. The length was measured with the FREEHAND LINE tool, in Z PROJECTION (maximum intensity) images. Branched spines were counted once.

Absolute changes of spine head area or PSD95 assembly area between two time points, t and t + 1, were calculated by ∆Aabs = A(t + 1)-A(t). ‘A’ denotes the spine head area or the PSD95 area, respectively. Normalized changes of spine head area or PSD95 area were computed by ∆Anorm=(A(t + 1)-A(t))/(A(t + 1)+ A(t)) and percentage changes by ∆A%=(A(t + 1)-A(t))/A(t)*100%.

The PCA was performed with the built-in MATLAB (MathWorks, Natick, MA) function ‘princomp’.

Pearson’s correlation coefficient and linear regression were computed in GraphPad Prism (version 7.04, GraphPad Software, San Diego, CA). The cross-correlation function was computed for different time intervals ∆t by

CC(Δt)=t, n=1N(A1 n(t+Δt)A1)(A2 n(t)A2)t, n=1N(A1 n(t)A1)2t, n=1N(A2 n(t)A2)2

A1 and A2 denote spine head and PSD95 assembly size, respectively. N stands for the total number of spines, ∆t the lag time, and A¯ the average size of all spine heads, respectively PSD95 assemblies.

Nanoorganization and morphological changes of PSD95

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All non-macular PSD95 nanoorganizations were selected. Their shape was categorized by three blinded scientists into perforated or clustered shapes. Perforated PSDs were of continuous shape but complex such as a ring or horseshoe-like or more twisted. Clustered nanoorganizations were characterized by two or more separated assemblies of PSD95 per spine and the number of clusters was counted. Temporal changes in PSD95 morphology were analyzed as follows. Assemblies which did not alter their overall morphology or number of clusters were marked as ‘no change’. Assemblies showing minor modifications, such as a small movement of a sub-cluster, were classified as ‘subtle change’. Changes of the overall morphology such as a smooth continuous PSD falling apart into different clusters were characterized as ‘strong change’. All changes were referred to time point t = 0 min.

Statistical analysis

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Statistical analysis was performed in GraphPad Prism. Positively skewed data sets were compared by the Kolmogorov-Smirnov test. Log-transformed data and normalized relative changes were normally distributed and compared by an unpaired t-test with Welch’s correction. The number of analyzed mice and spines, p-value, and the specific statistical test performed for each experiment are included in the appropriate figure legend. All tests were applied two-sided where applicable. Probabilities are symbolized by asterisks: *p < 0.05; **p < 0.01; ***p < 0.001, ****p < 0.0001.

Materials availability

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This study did not generate new unique reagents.

Data availability

Source data files of all analysed data are included in the submission.

References

    1. Panatier A
    2. Arizono M
    3. Nägerl UV
    (2014) Dissecting tripartite synapses with STED microscopy
    Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences 369:20130597.
    https://doi.org/10.1098/rstb.2013.0597
    1. Toni N
    2. Buchs PA
    3. Nikonenko I
    4. Povilaitite P
    5. Parisi L
    6. Muller D
    (2001)
    Remodeling of synaptic membranes after induction of long-term potentiation
    The Journal of Neuroscience 21:6245–6251.

Decision letter

  1. Yukiko Goda
    Reviewing Editor; RIKEN, Japan
  2. Lu Chen
    Senior Editor; Stanford University, United States

Our editorial process produces two outputs: i) public reviews designed to be posted alongside the preprint for the benefit of readers; ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.

Decision letter after peer review:

Thank you for submitting your article "Environmental enrichment enhances patterning and remodeling of synaptic nanoarchitecture revealed by STED nanoscopy" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Lu Chen as the Senior Editor. The reviewers have opted to remain anonymous.

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

The two essential revisions concern (1) clarifying the biological question being addressed and (2) validating the use of intrabody to follow the nano-structure of PSD95. The full reviews are appended below, which will help clarify the concerns of the individual reviewers, including the two points. In addition, the authors should address all the points raised in the individual reviews. The requested revision does not involve new experiments, and require mostly re-analysis of data and re-writing with clarifications and additional explanations.

Reviewer #1 (Recommendations for the authors):

This study capitalizes on the crosstalk-free two-color STED developed by the authors (Willg et al., Cell Rep 2021) to examine the dynamic changes in synapse structure in mouse visual cortex. Specifically, imaging the superficial dendrites of layer V pyramidal neurons, the authors compared how rearing mice in enriched environment (EE) affects spine morphology and the dynamics of PSD95 scaffolding protein within spines, compared to mice reared in control housing. Curiously, EE mice show less variable spine head volumes and PSD95 areas compared to control mice, while the spine head volume is larger but not PSD95 area in EE group compared to the control group. Moreover, nano-organization of PSD95 displays more prominent changes in EE compared to controls. The authors provide data of excellent quality, and the properties of subtle differences in the dynamics of spine head size and PSD95 organization would be of interest to cellular neuroscientists. Nevertheless, there is a large gap between spine structure dynamics and EE rearing. The causal relationship between the changes in PSD95 and spine size and the presumed enhanced sensory stimulation in EE should be better defined, or at least framed in a way that the results could be better interpreted. Moreover, given that the intrabody method to label PSD95 has not been used to examine nano-organization in detail, one should first validate the utility of the approach by establishing that it provides an accurate readout of the endogenous protein, for example, by using fluorescence immunolabelling to compare PSD95 and FingR signals.

1) That PSD95 intrabody has little effect on synapse organization has been examined at the level of conventional light microscopy. Given that the intrabody tagged with a fluorescent protein is of considerable size (~37 kD, according to Gross et al., 2013), could the authors exclude the possibility that there is no potential artefacts from steric hinderance, for instance?

2) To what extent does the amount of nanobody expressed per cell affect the dynamic behavior of PSD95? Could one be certain that there is no apparent relationship between the level of PSD95 intrabody expression per cell and PSD95 dynamics? In Figure S3, it would be more informative to compare the relationship between the brightness of PSD95 signal and the size of spines, the latter being measured by an independent signal. If the level of expression of PSD95 intrabody has no effect on PSD95 dynamics, then one would not expect to see a relationship, and this should be tested.

3) How does the PSD95 area measured using the intrabody compare to previous data obtained from PSD95-EGFP knock-in mice as reported in Wegner et al., 2018?

4) Figure 2C-J. Reduced variance in the sizes of spine head and PSD95 in the EE group seems to be due to the loss of smaller spine heads and PSD95 areas in the group. If one excludes the smallest spine heads and PSD95 areas, then is there any difference in the distribution?

5) Line 223. "However, whether dynamic changes between these two features are also strongly linked has remained unknown." Contrary to the statement, temporal uncoupling of spine head size changes in PSD95 increase has been noted previously (cf. Lines 48-53).

6) Figure 3G-H. The rationale for assessing the total variance of PSD95 area and spine size combined, for the comparisons between control and EE is not clear. Also, why does the variance for control show a peak at 60 min but decline at 120 min?

7) Figure 4F. As with the comment above, it is not clear why the slope of PSD95 area change should plateau at 60 min and show little increase at 120 min relative to 60 min for continuous baseline imaging.

Reviewer #2 (Recommendations for the authors):

1. The method is a nice advance that will be important for the field.

2. The motivation for the biological part of the study is lacking. The overarching question is not clear to me. One smaller question the authors are asking is if the correlation between PSD95 and spine head size is maintained in a short time window of plasticity. I am not sure why that is an important question. They also find that EE reduces variation in spine head size, but it is not clear the biological importance or consequences of a smaller variation in spine head size. Why is this an important analysis to do? The same can be said (noted below) about changes in PSD morphology. Throughout the paper, the authors should have the motivation for each analysis and link it back to their main overarching question. It is hard to say whether this study is an important biological advance because I am not sure what question they are really trying to address.

3. The introduction is really difficult to follow and reads a bit like a stream of consciousness. Please break it into paragraphs with themes. The introduction does not set up an overarching biological question and it should. Why have the authors done these particular experiments and analyses?

4. In a number of places in the paper, the language is difficult to read and at times overly complicated in structure. The authors often make style choices to not use commas surrounding explanatory clauses, but including commas would help with parsing many of the sentences. There are many typos throughout the manuscript, particularly with prepositions. A strong edit to make the language clear and direct would be very helpful for readers.

5. In line 84-85, the authors say that the dynamics of individual synapses in enriched environment are unknown. This is not entirely true. Yang et al., 2009 specifically looked at spine dynamics in vivo with enriched environment (PMID: 19946265), which should be cited here. Greifzu et al., 2014 also examines this general question in visual cortex by looking at E/I balance (and thus indirectly synapses) in enriched environments (PMID: 24395770). This study should also be cited.

6. In line 106-107, the authors say that 'Previous attempts featuring STED microscopy of EGFP and EYFP by two-color detection were suffering of high crosstalk requiring channel unmixing.' Could the authors please say what the issues were previously and what they have done to solve that problem? It is not clear to me, but the explanation would help highlight their methodological development.

7. As mentioned in the section above, I cannot find how long the authors waited after the cranial window surgery until they imaged, but if it is less than four weeks, they need to comment on the effects of inflammation on their synaptic results. This is critical for the interpretation.

8. In lines 287-289, the authors state that bigger spines tend to get smaller and smaller spines tend to get bigger. Given that there is a limit on spine head size, I think that the default hypothesis would be that this reflects regression to the mean. I am not sure why the authors have included this analysis, but they should either show controls that indicate it is not regression to the mean or remove this analysis from the manuscript.

9. As stated in the section above, it is not clear to me the biological relevance of changes in nanoorganization of PSD95. What are the biological consequences or significance of a shift in the nanoorganization for the function of the synapse? Also, could this analysis be quantiative, rather than just descriptive?

10. Lines 346-347, what does subtle change or strong change mean for a PSD95 morphology? Can this be quantified as a percentage change of some type? Could the authors also please explain the biological significance or consequences of this change?

11. In lines 457-458 and 472-473, the authors should cite the original paper that showed that in vivo scaling is input specific, Barnes et al. 2017 (PMID 24395770), not the review that they have cited here.

Reviewer #3 (Recommendations for the authors):

1) The data quality is amazing, it is very impressive that this resolution is possible in a breathing animal with a beating heart, using relatively slow scanning microscopy. You should mention the complex procedure you developed to ensure stability, normal orientation and biocompatible surface of the cranial window, pointing to the Methods paper for detail. You have earned your bragging rights.

2) What kind of anesthesia was used during imaging? How much time elapsed between the onset of anesthesia and the first imaging time point (t = 0 min)? Were the imaging experiments performed blind with respect to the housing conditions?

3) lines 235-238: "This means..." This sentence is confusing, delete. The correlation is clearly described in the next sentence. Same for the figure title: "positively and negatively correlated" - I see only weak positive correlations on the population level.

In general, Fig. 3 is a bit confusing due to the separate analysis of 3 time points, but no discussion about what happened at t = 0 (onset of anesthesia?). Therefore, the reader is left wondering if the fact that correlation and variance are more or less tight at different time points carries any biological relevance, or if these are supposed to be repeated measures of a Kesten process at work, or perhaps a control for stable imaging conditions? If this is about detecting differences between EE and control, wouldn't it make more sense to pool all time points?

4) Fig 4F, control animals: I have a hard time understanding how there can be shrinkage at 30 min sampling intervals, but not at longer intervals. Does the dt30min group only contain (30 min - 0 min), or also (60 min - 30 min)? Does this mean initially shrunken PSDs grow back again? Has this something to do with the onset of anesthesia? Please explain/interpret this result.

5) Fig. 4G: This analysis is great, but its significance might be difficult to understand for some readers. It might be worth pointing out that if there were a temporal sequence, e.g. first spine size expansion, then PSD enlargement, this would result in little correlation when comparing just two time points. Thus, the cross-correlation analysis. One could even do a little simulation to illustrate how the cross correlation would look like if changes were linked with a delay (this is optional).

6) Did some spines disappear completely during the period of observation?

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

Thank you for resubmitting your work entitled "Environmental enrichment enhances patterning and remodeling of synaptic nanoarchitecture as revealed by STED nanoscopy" for further consideration by eLife. Your revised article has been evaluated by Lu Chen (Senior Editor) and a Reviewing Editor.

The manuscript has been significantly improved but there are some remaining issues, mostly concerning aspects of the biological context of the study, that need to be addressed, as outlined below:

Line 32-33: "… synaptic strength is set more precisely."

The term "precise" implies that there is a specific value around which the synaptic strength is set, which is not clear in this case. It is suggested that the authors use a more neutral expression such as ".. synaptic strength is set more uniformly" to describe the key observation.

Line 62: "… which was confirmed recently (Holler et al., 2021)."

It would be informative to indicate that the study involved EM (CLEM) analysis, and should be rephrased, for example, "… which was confirmed recently at ultrastructural resolution..".

Line 67: "… directly after LTP."

->.. directly after LTP induction.

Line 70: "… with a delay of ~1 hour after LTP"

As with above, potentiation itself can last for hours, and thus the timeframe being referred to needs to be clear.

-> e.g. … with a delay of ~1 hour after inducing LTP

Line 76: "… of the spine and postsynapse at increased activity and baseline in vivo."

The increased activity is an assumption, and this should be reflected in the statement. For example, one could rephrase along the lines of "… of the spine and postsynapse in vivo using mice reared in enriched environment representing increased activity conditions and normal housing representing baseline conditions."

Line 123

Is it meant that in one possibility, EE could directly affect "pre-existing" synapses rather than "all" synapses?

Line 190

In addition to the reference to Steffens et al., 2020, it would be helpful to the reader if the authors briefly mention what particular procedural features help ensure the craniotomy be atraumatic as possible.

Line 580: "To study activity-dependent changes, we compared adult mice housed in standard cages with age matched EE housed mice and analyzed synaptic morphology and plasticity …."

The motivation for the study is somewhat misguided and unclear, since the present work assessed the influence of activity by subjecting mice to activity-enhanced conditions by means of EE, and did not directly examine activity-dependent changes per se. Moreover, as acknowledged by the authors, there may be an issue also of anesthesia. The starting sentence as well as the rest of the discussion in the paragraph need to reflect these points.

Paragraph starting from Line 606

As with the comment above, the caveats of the present experimental design, including the effects of anesthesia, should be acknowledged.

https://doi.org/10.7554/eLife.73603.sa1

Author response

The two essential revisions concern (1) clarifying the biological question being addressed and (2) validating the use of intrabody to follow the nano-structure of PSD95. The full reviews are appended below, which will help clarify the concerns of the individual reviewers, including the two points. In addition, the authors should address all the points raised in the individual reviews. The requested revision does not involve new experiments, and require mostly re-analysis of data and re-writing with clarifications and additional explanations.

We would like to thank all reviewers for acknowledging our work and for their thoughtful comments and constructive criticism. The remarks were very helpful and improved the manuscript. We have addressed all points raised below and hope the reviewers will find our revised version suitable for publication.

Reviewer #1 (Recommendations for the authors):

This study capitalizes on the crosstalk-free two-color STED developed by the authors (Willg et al., Cell Rep 2021) to examine the dynamic changes in synapse structure in mouse visual cortex. Specifically, imaging the superficial dendrites of layer V pyramidal neurons, the authors compared how rearing mice in enriched environment (EE) affects spine morphology and the dynamics of PSD95 scaffolding protein within spines, compared to mice reared in control housing. Curiously, EE mice show less variable spine head volumes and PSD95 areas compared to control mice, while the spine head volume is larger but not PSD95 area in EE group compared to the control group. Moreover, nano-organization of PSD95 displays more prominent changes in EE compared to controls. The authors provide data of excellent quality, and the properties of subtle differences in the dynamics of spine head size and PSD95 organization would be of interest to cellular neuroscientists. Nevertheless, there is a large gap between spine structure dynamics and EE rearing. The causal relationship between the changes in PSD95 and spine size and the presumed enhanced sensory stimulation in EE should be better defined, or at least framed in a way that the results could be better interpreted. Moreover, given that the intrabody method to label PSD95 has not been used to examine nano-organization in detail, one should first validate the utility of the approach by establishing that it provides an accurate readout of the endogenous protein, for example, by using fluorescence immunolabelling to compare PSD95 and FingR signals.

We thank the reviewer for the valuable comments. We have refined the introduction and explained our motivation in more detail. We also have justified the accuracy of the PSD.FingR label. Please find the details below.

1) That PSD95 intrabody has little effect on synapse organization has been examined at the level of conventional light microscopy. Given that the intrabody tagged with a fluorescent protein is of considerable size (~37 kD, according to Gross et al., 2013), could the authors exclude the possibility that there is no potential artefacts from steric hinderance, for instance?

This aspect is indeed vital to our overall approach. We absolutely agree that steric hindrance, or labelling density in general is a major problem that is particularly evident in superresolution microscopy. Gross et al., 2013 show nicely that “the expression patterns of their endogenous target proteins or the number or strength of synapses” was not affected by PSD95.FingR. It is very difficult to prove whether there are potential artefacts from steric hindrance, which were not recognized by this original publication. However, we think that PSD95.FingR excellently recognizes the PSD95 nanopattern without artefacts for the following reasons. First, we double labeled PSD95 with another intrabody, PF11 which shows a very high overlap with PSD95.FingR (Author response image 1). Second, the PSD95.FingR labeled nanostructure of endogenous PSD95 is very similar to our previously recorded in vivo STED data of a PSD95-EGFP knock-in mouse (Author response image 2). Third, the nanopattern of PSD95, which we observe is very similar to the perforated post-synaptic density of electron microscopy images (Arellano et al., 2007; Harris and Weinberg, 2012; Toni et al., 2001). Forth, FingR.PSD95 is less than half the size of PSD95 (cf. Figure 2G, (Gross et al., 2013)). Together, this indicates that PSD95.FingR is an excellent label of PSD95.

Author response image 1
Double labelling of PSD95 with two different intrabodies, PSD95.Fing and PF11.

PF11 recognizes palmitoylated PSD95 (Fukata et al., 2013) and was cloned with the hSyn promoter and the orthogonal transcriptional regulation IL2RGTC (Gross et al., 2013) in the same pAAV backbone as PSD95.FingR. PF11 fused to EGFP and PSD95.FIngR fused to Citrine were co-expressed in cultured hippocampal neurons as described in (Wegner et al., 2017) and imaged with our two-color setup in confocal mode. Both labels show a very high degree of co-localization. Of note, in our hands the expression level of PF11 was not bright enough to record superresolution STED images but PSD95 nanodomains with PF11 labeling were shown in (Fukata et al., 2013). PF11 was a gift from Masaki Fukata, National Institute for Physiological Sciences, Japan.

Author response image 2
Side-by-side comparison of two different labelling schemata of PSD95 imaged by in vivo STED microscopy.

(A) Selection of PSD95 assemblies showing a nanopattern of PSD95-EGFP knock-in mouse as published in the supplementary material of (Wegner et al., 2018). (B) PSD95.FingR-Citrine (green) and membrane label (magenta) which were analyzed for Figure 5, this manuscript (Images are included in Figure 5–source data 1). (A, B) All images were recorded in the visual cortex of an anaesthetized mouse. The PSD95 nanopattern of the EGFP knock-in mouse (A) and viral expression of PSD95.FingR-Citrine (B) are very similar.

We added the following to the discussion: “The pattern of the PSD95 nanoorganization labeled with PSD95.FingR was very similar to the structures we have observed earlier in a PSD95-EGFP knock-in mouse with in vivo STED microscopy (Wegner et al., 2018). This nanopattern is also similar to perforated postsynaptic densities reported by electron microscopy (Arellano et al., 2007; Harris and Weinberg, 2012; Toni et al., 2001).”

2) To what extent does the amount of nanobody expressed per cell affect the dynamic behavior of PSD95? Could one be certain that there is no apparent relationship between the level of PSD95 intrabody expression per cell and PSD95 dynamics? In Figure S3, it would be more informative to compare the relationship between the brightness of PSD95 signal and the size of spines, the latter being measured by an independent signal. If the level of expression of PSD95 intrabody has no effect on PSD95 dynamics, then one would not expect to see a relationship, and this should be tested.

We thank the reviewer for this valuable comment. It is a very difficult endeavor to conscientiously prove that the amount of PSD95.FingR expression has no effect on the PSD95 dynamics. However, for the following reasons we think that this aspect does not influence our results. First, due to the transcriptional regulation system we employ for the PSD95.FingR expression (see Author response image 3 for explanation) we did not detect a significant variation in PSD95 brightness between cells, which is indeed a common problem with conventional overexpression of fusion proteins.

We explain this system now in the Result section.” Second, the morphological changes of the PSD95 nanopattern were analyzed in the same way as in our previous publication of the PSD95-EGFP knock-in mouse (cf. Figure 2E, (Wegner et al., 2018)) and we found similar temporal changes for our Ctr mice. Third, we used the same batch of purified virus for all experiments shown, i.e. the same virus concentration; EE and Ctr mice were labeled by exactly the same protocol.”

We added this information to the Methods. “Fourth, we studied temporal changes on the time scale of 30 to 120 minutes which is relatively slow, much slower than a free diffusion which would be on a time scale below one second. Thus, we do not expect an influence of the attached label on such a slow nanoplasticity.”

Unfortunately, we did not measure the spine size by another, independent signal. In addition, as shown in Figure 2B, spine head area and PSD95 area only show a correlation coefficient of ~ 0.8 and thus such a correlation as suggested by the reviewer would be rather coarse.

Author response image 3
Transcriptional regulation of PSD95.FingR.

(A) PSD95.FingR expression is controlled by a negative feedback regulation so that once endogenous PSD95 binding sites are saturated, unbound PSD95.FingR moves to the nucleus due to a nuclear localization sequence that is part of the CCR5 zinc finger domain. Binding of the repressor KRAB-A to the promoter via the zinc finger inhibits further transcription and expression of PSD95.FingR. Thus we expect a similar brightness of the PSD95.FingR label when in saturation. Adapted from Figure 3G in (Gross et al., 2013). (B) After an in vivo experiment the mouse was perfused and the brain was sliced. A confocal image of layer 5 cortex shows a bright Citrine label of unbound PSD95.FingR in the nucleus indicating that the PSD95 binding sites in this pyramidal neuron were saturated. Since all measurements were performed at the same conditions we expect a similar brightness of PSD95 in all images.

3) How does the PSD95 area measured using the intrabody compare to previous data obtained from PSD95-EGFP knock-in mice as reported in Wegner et al., 2018?

Unfortunately, the sizes of these two datasets cannot be compared in absolute terms for the following reasons. In the knock-in mouse, PSD95 is ubiquitously labeled, i.e. all cell types which express PSD95 will be fluorescently tagged. In layer 1 this includes apical dendrites of layer 5 and layer 2/3 pyramidal neurons as well as interneurons. The PSD95.FingR encoding AAV, however, was injected into layer 5 and thus we imaged mainly layer 5 apical dendrites in layer 1. Spine head and PSD sizes are highly variable within one cell, but averages of these values also change between cell type and brain area. For example, spine heads of layer 5 are larger than those of layer 2/3 (Konur et al., 2003) and the PSD area average size is different between brain regions (Table 1, (Harris and Weinberg, 2012)). Therefore, sizes of the layer 5 PSD95 labeled with FingR cannot be compared with the ubiquitous PSD95 knock-in label.

Another difficulty in comparing these datasets is the different method how the PSD95 area was analyzed. In Wegner et al. we measured only the length and width (width was measured only for some assemblies) and averaged these values. For the PSD95.FingR we refined this method and encircled now each PSD95 assembly, which is more accurate especially for the complex PSD95 shapes. Thus, we determined an average diameter in Wegner et al. 2018, but an average area for the PSD95.FingR. However, in a first draft of the current manuscript we have analyzed the length and width of the PSD95 assemblies similarly to Wegner et al. 2018. For comparison, we show a histogram of this early length analysis in Author response image 4. It shows that the PSD95 assemblies of layer 5 apical dendrites are larger than that of the PSD95 knock-in mouse which most likely reflects the large spine heads of layer 5 neurons (Konur et al., 2003) as discussed above.

Author response image 4
PSD95 assembly size from in vivo STED measurements in layer 1 mouse visual cortex for different cell types.

(A) Size/length measurement of ubiquitous PSD95 assemblies of a PSD95-EGFP knock-in mouse as published in Wegner et al., 2018. (B) Average of length and width of PSD95 assemblies of layer 5 pyramidal neurons labeled with PSD95.FingR. Data set Ctr mice, first time point.

To address this difference, we added to the Result section: “This size of the PSD95 area of layer 5 pyramidal neurons is slightly larger than our previously reported diameter of 354 nm which corresponds to ~ 0.10 µm2 for a circular distribution, obtained in a ubiquitously expressing PSD95-EGFP knock-in mouse (Wegner et al., 2018); therefore, the larger size of the PSD95 area could reflect the larger size of the spine heads of layer 5 pyramidal neurons (Konur et al., 2003).”

4) Figure 2C-J. Reduced variance in the sizes of spine head and PSD95 in the EE group seems to be due to the loss of smaller spine heads and PSD95 areas in the group. If one excludes the smallest spine heads and PSD95 areas, then is there any difference in the distribution?

A reduction in variance means that there are fewer extreme values. The reduction in variance comes along with an increase in spine head size for EE mice which indicates indeed a loss of smaller spine heads. We agree with the reviewer on this point. However, this can already be seen well in Figure 2E; therefore we prefer to show only the entire distribution. Since there is no definition on what is a small spine, a cut-off would be rather arbitrary and change the shape of the distribution.

We included this observation into the Result section: This is manifested by a significantly larger spine head area (Figure 2G) and smaller variance of the size distribution (Figure 2I) of EE housed mice, which might imply a preferential loss or adaptation of small spines (Figure 2E).

5) Line 223. "However, whether dynamic changes between these two features are also strongly linked has remained unknown." Contrary to the statement, temporal uncoupling of spine head size changes in PSD95 increase has been noted previously (cf. Lines 48-53).

Thank you for this comment. This sentence now reads: However, on which time scale dynamic changes between these two features are linked in vivo has remained unknown.

6) Figure 3G-H. The rationale for assessing the total variance of PSD95 area and spine size combined, for the comparisons between control and EE is not clear. Also, why does the variance for control show a peak at 60 min but decline at 120 min?

The total variance is a measure for the overall variability of the two parameters, spine head size and PSD95 area; it shows that these parameters are more variable in Ctr than EE housed mice. However, since the total variance is just the sum of the variances for PC1 and PC2 this information is redundant and we moved the total variance to the extended figure. Instead, we took up comment “minor point 3” of reviewer 3 and summed up the different time intervals to more clearly show the difference between EE and Ctr housed mice. While the variance is lower for both, PC1 and PC2 in EE housed mice this decrease is highly significant only for PC2, which indicates a strong decrease in negatively correlated changes. Cf. also comment to reviewer 3, added panel Figure 3G, J.

The peak of the variance at 60 min and decline at 120 min for Ctr is not statistically significant and thus this peak might be just a statistical fluctuation. For a conservative interpretation of the data, we only claim that the variance of PC1 and PC2 is larger for Ctr at all time intervals and anti-correlated changes represented by PC2 are significantly increased for Ctr mice (Figure 3J)

7) Figure 4F. As with the comment above, it is not clear why the slope of PSD95 area change should plateau at 60 min and show little increase at 120 min relative to 60 min for continuous baseline imaging.

We agree with the reviewer that the reason for the changes of the slopes is not clear; in particular, it is not clear why the slope increases for Ctr. Our measurements are to our knowledge the first showing such fluctuations in size in vivo and simulations of these temporal changes are difficult since the mechanism which drives these changes is not known in detail. However, the overall differences between EE and Ctr fit very well with what was observed for the synaptic size and fluctuation parameters in a silenced network (Hazan and Ziv, 2020). Hazan and Ziv found that synaptic silencing increased the synaptic size distribution in mean size and width, as well as weakened the multiplicative descaling. Our model of increased activity by EE leads to an opposing effect; the synaptic size distribution is narrower and the multiplicative descaling increases, i.e. smaller slope for PSD95 at 60 min and 120 min intervals which could explain their narrower size distribution. Future studies will follow to examine these size changes also at longer time intervals, for example by combining with our recently established chronic imaging method (Steffens et al., 2021)

We have refined the discussion on this topic in Discussion section “Stronger multiplicative downscaling in EE housed mice for PSD95”

Reviewer #2 (Recommendations for the authors):

1. The method is a nice advance that will be important for the field.

We thank the reviewer for acknowledging our progress on two-color in vivo STED microscopy.

2. The motivation for the biological part of the study is lacking. The overarching question is not clear to me. One smaller question the authors are asking is if the correlation between PSD95 and spine head size is maintained in a short time window of plasticity. I am not sure why that is an important question. They also find that EE reduces variation in spine head size, but it is not clear the biological importance or consequences of a smaller variation in spine head size. Why is this an important analysis to do? The same can be said (noted below) about changes in PSD morphology. Throughout the paper, the authors should have the motivation for each analysis and link it back to their main overarching question. It is hard to say whether this study is an important biological advance because I am not sure what question they are really trying to address.

We regret that our introduction and motivation was not clear enough. However, we thank the reviewer for asking these critical questions which have helped us to restructure the introduction and to include new aspects to the discussion. Our response point-by-point:

- Overarching question?

There is general consensus that acquisition of memory activates or forms a specific assembly of synapses. Thus, spines emerge, disappear, or change with cellular processes underlying learning, and even “remember” previous sensory experience (Poo et al., 2016). It is also clear that learning induces structural and functional synaptic changes similar to long-term potentiation (LTP) protocols. In this concept of learning, however, the maintenance of memory critically depends on the stability of the underlying synaptic connections. However, there is substantial evidence that synaptic structures are highly volatile intrinsically as such that synaptic connections undergo continuous spontaneous remodeling without any activity. Previous in vivo studies have focused primarily on the persistency of spines and synapses in terms of their appearance and elimination and estimated the spine size from fluorescence intensity measurements; directly assessed changes in synapse or spine head size and visualizations of the synaptic nanoorganization in vivo are missing. With our approach of superresolution two-colour in vivo STED microscopy we address with nanoscale resolution (1) the plasticity of spine heads and synapses at baseline at time scales similar to LTP processes; (2) the correlation between PSD95 and spine head size changes; (3) the plasticity of the PSD95 nanoorganization; and (4) whether enhanced activity changes the structure and/or plasticity of these measures.

- Why is the correlation between PSD95 and spine head size important?

in vitro experiments have shown a temporal detuning of PSD95 and spine head size after glutamate uncaging (Meyer et al., 2014). We set out to determine whether such a temporal shift also occurs at baseline in vivo for two reasons: In the literature both parameters, spine heads and PSD95, are often used as a measure for the synaptic strength since their size can be determined relatively easily and repetitively in vivo for large sets of data. Strongly uncorrelated changes would raise the question of which parameter is the better correlate for synaptic strength. Secondly, AMPAR mediated currents were shown to increase simultaneously with the spine head directly after LTP. Given that AMPARs are anchored to the synapse at PSD95 via TARP, a temporal decorrelation between spine head and PSD95 accumulation size suggests that the number of PSD95 is not the rate limiting parameter for (short-term) synaptic plasticity; thus PSD95 might provide slots for AMPAR which are activated by an unknown mechanism on stimulation.

- Smaller variation in spine head/PSD95 area? Biological consequence?

The most widely studied form of activity dependent synaptic structural changes is chronic silencing or deprivation of synaptic activity which is compensated by a multiplicative increase in the strength of excitatory synapses, characterized by an increase in average synapse or spine size and broadening of the distribution. As such, for example, described for deprivation in vivo (Keck et al., 2013) or silenced cortical neuronal cultures (Hazan and Ziv, 2020). Interestingly, we did not find such a multiplicative scaling of synaptic or spine head sizes when comparing enhanced-activity EE and Ctr housed mice.

However, a decrease in variability was described before for the neuronal firing rates after stimulation. This was observed in different brain regions and even when the change in mean firing rate was little (Churchland et al., 2010). The authors of this study conclude that the variance decline of the firing rate implies that cortical circuits become more stable. The same might apply to our observation of smaller variance of synapse sizes; we observe less extreme values, a smaller variation of size changes and less negatively correlated changes for EE housed mice which might suggest that sizes are better defined. Thus, the neuronal network might be of higher stability after training in the enriched environment.

Further experiments are certainly needed to clarify the biological impact of these findings; for example, how do spine heads and PSD95 change in other brain regions such as in the motor cortex or in other cortical layers? How much does the EE change the activity in the visual cortex? Which implication does the variance of these sizes have on models of synaptic stability?

- Why are changes in PSD morphology important?

Perforated PSD95 nanoorganizations as well as AMPA receptors are found mainly on large spines. AMPAR are anchored to PSD95 via stargazin/TARP. Simulations have shown that AMPAR current amplitude drops significantly already at 50 nm offset between presynaptic glutamate release side and AMPA cluster (Haas et al., 2018). Changes in the PSD95 nanoorganization might therefore be a fast mechanism to align postsynaptic receptor to presynaptic release sites.

We have restructured the introduction and complemented the discussion to include these valuable points

3. The introduction is really difficult to follow and reads a bit like a stream of consciousness. Please break it into paragraphs with themes. The introduction does not set up an overarching biological question and it should. Why have the authors done these particular experiments and analyses?

We have revised the introduction and subdivided into paragraphs with themes as suggested.

4. In a number of places in the paper, the language is difficult to read and at times overly complicated in structure. The authors often make style choices to not use commas surrounding explanatory clauses, but including commas would help with parsing many of the sentences. There are many typos throughout the manuscript, particularly with prepositions. A strong edit to make the language clear and direct would be very helpful for readers.

We have revised the language.

5. In line 84-85, the authors say that the dynamics of individual synapses in enriched environment are unknown. This is not entirely true. Yang et al., 2009 specifically looked at spine dynamics in vivo with enriched environment (PMID: 19946265), which should be cited here. Greifzu et al., 2014 also examines this general question in visual cortex by looking at E/I balance (and thus indirectly synapses) in enriched environments (PMID: 24395770). This study should also be cited.

We thank the reviewer for pointing out these references which we included it in the introduction of structural changes and synaptic plasticity by EE.

However, Yang et al. focused on the spine turnover and we refer in our manuscript with “dynamics of individual synapses” to the spine and synapse substructure of the whole ensemble of spines, including persistent spines. According to Yang et al. and others, a large fraction of the spines persists throughout life. We hope we could clarify that aspect in the introduction.

6. In line 106-107, the authors say that 'Previous attempts featuring STED microscopy of EGFP and EYFP by two-color detection were suffering of high crosstalk requiring channel unmixing.' Could the authors please say what the issues were previously and what they have done to solve that problem? It is not clear to me, but the explanation would help highlight their methodological development.

We added to the results: “The challenge for in vivo two-color STED microscopy is to find an in vivo compatible pair of fluorescent molecules with similar emission spectra so that it can be depleted with the same STED beam, but at the same time can be temporally or spectrally separated (Willig et al., 2021). Previous attempts featuring STED microscopy of EGFP and EYFP utilized a single excitation wavelength and two-color detection, which suffered from high crosstalk, and therefore required a linear unmixing of channels (Tønnesen et al., 2011). Channel unmixing, however, requires large signal to noise levels. To reduce crosstalk and thus avoid the necessity of channel unmixing, we extended our previously described in vivo STED microscope (Willig et al., 2014) by an additional two-color excitation and detection to selectively excite the green or yellow fluorescent protein (Figure 1A).”

7. As mentioned in the section above, I cannot find how long the authors waited after the cranial window surgery until they imaged, but if it is less than four weeks, they need to comment on the effects of inflammation on their synaptic results. This is critical for the interpretation.

We performed acute experiments. As such, we performed the imaging directly after implanting the window and sacrificed the mouse after the session. We performed the cranial window procedure as atraumatic as possible and did not observe tissue damage.

We added: … “mice were anesthetized and a cranial window was implanted above the visual cortex. Imaging commenced about 2.5 hours after onset of the anesthesia.”

8. In lines 287-289, the authors state that bigger spines tend to get smaller and smaller spines tend to get bigger. Given that there is a limit on spine head size, I think that the default hypothesis would be that this reflects regression to the mean. I am not sure why the authors have included this analysis, but they should either show controls that indicate it is not regression to the mean or remove this analysis from the manuscript.

The reviewer addresses here a valuable point. When small spines tend to increase and bigger spines tend to get smaller this reflects indeed regression to the mean and is a common statistical phenomenon. However, this does not affect the interpretation of our data because we compare the changes between Ctr and EE housed mice for the same time intervals and for the whole population of spines. Moreover, we always analyze the whole population and do not group spines into big or small. Analyzing changes exclusively in small or large spines would indeed result in an artifact due to regression to the mean.

In detail, the linear regression such as plotted in Figure 4A–E could be also regarded as a measure for the regression to the mean. The more it diverges from the line of unity, the stronger is the regression toward the mean. As such our data are indeed a measure for the regression toward the mean which is significantly different for PSD95 between EE and Ctr housed mice (Figure 4F).

In the old line 287-289 we refer to our old Figure S5 (new Figure 4—figure supplement 1). In this figure we are plotting the size changes instead of the absolute values and thus it is just another visualization of Figure 4. We would prefer to retain the figure because it links our results to other papers on synaptic size dynamics, e.g. Figure 2 in (Statman et al., 2014) or (Ziv and Brenner, 2018).

We added to the results: “Such a tendency is often called regression to the mean and is an often observed statistical phenomenon. However, it is driven by biological processes, and the strength of those changes may vary under different conditions such as between EE and Ctr. To quantify these changes in synapse and spine head size we use a Kesten process …”

9. As stated in the section above, it is not clear to me the biological relevance of changes in nanoorganization of PSD95. What are the biological consequences or significance of a shift in the nanoorganization for the function of the synapse? Also, could this analysis be quantiative, rather than just descriptive?

As stated in point 2 above, we hypothesize that changes in the PSD95 nanoorganization may cause changes in the alignment of glutamate receptors with the presynapse and thus influence synaptic strength.

The PSD95 nanoorganization is very complex; we often observe clusters but also continuous structure of horseshoe or more complex shapes (See Author response image 2). Previous studies have analyzed numbers of nanodomains (Hruska et al., 2018) which would be possible with relatively simple routines. However, we think that this does not satisfactorily reflect the complexity of the structure. In the future we will develop a shape analysis tool to quantitatively analyze such nanoorganizations. We added all image sections of nanoorganizations to the supplement (Figure 5–source data 1) so that the reader can get an impression about the diversity of shapes.

We included a comment on this issue to the discussion and results.

10. Lines 346-347, what does subtle change or strong change mean for a PSD95 morphology? Can this be quantified as a percentage change of some type? Could the authors also please explain the biological significance or consequences of this change?

We have included more details to the introduction and discussion.

11. In lines 457-458 and 472-473, the authors should cite the original paper that showed that in vivo scaling is input specific, Barnes et al. 2017 (PMID 24395770), not the review that they have cited here.

We have exchanged the reference.

Reviewer #3 (Recommendations for the authors):

1) The data quality is amazing, it is very impressive that this resolution is possible in a breathing animal with a beating heart, using relatively slow scanning microscopy. You should mention the complex procedure you developed to ensure stability, normal orientation and biocompatible surface of the cranial window, pointing to the Methods paper for detail. You have earned your bragging rights.

We are happy to read that the reviewer appreciates our superresolution in vivo images. The preparation is indeed the result of years of development.

We added to the results: “To perform motion and aberration free imaging at nanoscale resolution the cranial window needs to be of highest quality. As described in detail in (Steffens et al., 2020) critical steps involve a craniotomy which is as atraumatic as possible, a negligible small gab between brain surface and cover glass and the right choice of the dental cement to avoid bending of the cover glass.”

2) What kind of anesthesia was used during imaging? How much time elapsed between the onset of anesthesia and the first imaging time point (t = 0 min)? Were the imaging experiments performed blind with respect to the housing conditions?

We used MMF, a mixture of fentanyl, midazolam, and medetomidin for imaging and cranial window preparation (cf. Method section).

We added the time between onset of the anesthesia and start of the imaging to the result section: “Imaging commenced about 2.5 hours after onset of the anesthesia.”

The imaging was not performed blind. We ordered the mouse from the animal facility and thus knew which one it was. However, Ctr and EE housed mice were imaged in random order and the dendrites were picked randomly. The analysis was performed blindly. We added these details to the methods section.

3) Lines 235-238: "This means..." This sentence is confusing, delete. The correlation is clearly described in the next sentence. Same for the figure title: "positively and negatively correlated" - I see only weak positive correlations on the population level.

We agree that the sentence “This means…” is partially redundant with the subsequent sentence and therefore deleted it. We also agree that the correlation is positive on the population level and changed the figure title.

In general, Fig. 3 is a bit confusing due to the separate analysis of 3 time points, but no discussion about what happened at t = 0 (onset of anesthesia?). Therefore, the reader is left wondering if the fact that correlation and variance are more or less tight at different time points carries any biological relevance, or if these are supposed to be repeated measures of a Kesten process at work, or perhaps a control for stable imaging conditions? If this is about detecting differences between EE and control, wouldn't it make more sense to pool all time points?

We thank the reviewer for pointing out that it is unclear why we performed the time lapse for the three time intervals and what we expected. T = 0 marks the first imaging measurement about 2.5 hours after onset of anesthesia and cranial window preparation (we clarified that cf. remark 2).

Meyer et al (Meyer et al., 2014) have shown that the brightness of fluorescently labeled PSD95 increased slowly over 180 min after glutamate uncaging. Bosch et al. (Bosch et al., 2014) found that the postsynaptic scaffolding proteins Homer1b and Shank1b persistently increased for up to 150 min after LTP. Thus we hypothesized that we would find larger changes in PSD95 area after 2 hours (Fig. 3F) than after 30 min (Fig. 3D). The most straightforward way to analyze these changes is to compute the variance of the changes. Fig. 3 D–J shows that we did not find a statistically significant change in variance between the different time intervals for each group. This changed when we plotted these changes specifically over the original size (Figure 4–figure supplement 1) or the size at t+Δt over the size at t as shown in Fig. 4. In this way, we found strong differences in the slope of the PSD95 area changes (Fig. 4F). The slope is one of the two parameters which are used as variables in the Kesten process (nicely explained in (Ziv and Brenner, 2018)). Previous experiments and simulations of the Kesten process have shown that a decrease in this slope, termed multiplicative downscaling, results in a narrower synaptic size distribution which is exactly what we observe for PSD95. We have tried to emphasize this aspect more in the manuscript. Thus, we found indeed differences in the temporal changes between different time points (Fig. 4), but they are not visible in the simple analysis of variance (Fig. 3).

However, we took up the last suggestion of the reviewer and pooled the variances for the different time intervals per group. The variance of the Ctr group is larger for both PC1 and PC2, however, this difference is highly significant only for PC2.

Therefore, we added: “This means that the negatively correlated changes contribute much less than the positively correlated changes in the EE housed mice indicating a stronger, positive coupling between changes in spine head size and PSD95 area.”

We add panel G and J to Fig. 3.

4) Fig 4F, control animals: I have a hard time understanding how there can be shrinkage at 30 min sampling intervals, but not at longer intervals. Does the dt30min group only contain (30 min - 0 min), or also (60 min - 30 min)? Does this mean initially shrunken PSDs grow back again? Has this something to do with the onset of anesthesia? Please explain/interpret this result.

A small misunderstanding may have occurred here. A small value for the slope does not necessarily mean shrinkage. The linear regression lines for all measures (Fig. 4A, B, D, E) have a positive y-intersect and a slope <1 which indicates that small spines preferentially grow while large spines preferentially shrink. The average value is roughly stable (Fig. 3C). Thus, we observe a size dependent, multiplicative component which is indicated by a slop ≠1 and an additive component indicated by a positive y-intersect. This fits to a Kesten process which is a statistical framework for a stochastic process combining a multiplicative downscaling, and an additive growth (Statman et al., 2014); we have performed measurements at baseline, i.e. steady state, at which the average size is constant.

However, we cannot fully explain this increase in slope for the Ctr mice. Previous experiments have shown a steady decrease of the linear regression slope over time as shown in (Hazan and Ziv, 2020)(Statman et al., 2014). However, these studies were performed in cultured neurons over a very long temporal range of up to 50 hours, by using overexpression of PSD95-GFP, and the fluorescence brightness was taken as a measure for size. The measurements were smoothed over different time points, precluding an analysis between consecutive time points / short time intervals. It is also conceivable that such highly regulated changes follow a different time line in vivo. In the future we will use our recently established chronic STED imaging (Steffens et al., 2021) to follow the PSD95 size dynamic also on longer time scales to analyze the long term dynamic such as in (Statman et al., 2014).

We have refined the discussion section “Stronger multiplicative downscaling in EE housed mice for PSD95”

Imaging was performed about 2.5 hours after the onset of the anesthesia and thus should be in a stable regime regarding the anesthesia. We added this information to the result section.

We indeed pooled the data, i.e. for the 30 min interval, we included 0–30 min and 30–60 min changes. The rational to do that is that we measured at baseline, i.e. we did not stimulate and measured long after onset of the anesthesia. However, to show that pooling did not affect the outcome of the study, we also analyzed the first interval only (0–30 min, 0–60 min and 0–120 min). As shown in Author response image 5, the first interval only provided similar results as the pooled data, just with larger error bars:

Author response image 5
Slope of the linear regression as shown in the manuscript Fig.4C, F for pooled data and first time interval only.

The pooled data includes 0–30 min and 30–60 min data for Δt = 30 min. Δt = 60 min includes 0–60 min and 60–120 min of the hourly measurement interval and 0–60 min of the half hour measurement series. Pooled data for Δt = 120 min includes 0–120 min of the 2 hour time interval measurement and 0–120 min of the hourly measurement series. For comparison, we computed the linear regression for the first interval only, which are 0–30 min, 0–60 min and 0–120 min (right). There were no major differences between the pooled data (left) and the data for the first interval (right), except for a much larger error bar for the 120 min interval..

We added a comment on the pooling to the legend of Fig. 3: “Time intervals are pooled; e.g. Δt = 30 min includes 0–30 min and 30–60 min.”

5) Fig. 4G: This analysis is great, but its significance might be difficult to understand for some readers. It might be worth pointing out that if there were a temporal sequence, e.g. first spine size expansion, then PSD enlargement, this would result in little correlation when comparing just two time points. Thus, the cross-correlation analysis. One could even do a little simulation to illustrate how the cross correlation would look like if changes were linked with a delay (this is optional).

We agree and thank the reviewer for pointing this out. We have separated this paragraph for clarity and added a motivation:

“No temporal shift between spine head and PSD95 area changes at baseline

We found a strong correlation coefficient of ~0.8 between spine head and PSD95 area (Figure 2B). However, if the PSD expands with a temporal delay of ~1 hour to the spine head as suggested by the work of Bosch et al. and Meyer et al. (Bosch et al., 2014; Meyer et al., 2014), the correlation should be even higher when comparing spine heads and PSD95 area at different time points. Therefore, we computed the cross-correlation between these measures for all time intervals.”

6) Did some spines disappear completely during the period of observation?

We thank the reviewer for pointing out this interesting question. We saw indeed some protrusions appearing and disappearing during the observation period. All of these new or lost protrusions, however, did not bear PSD95 puncta and did not show a thickening at the end, a spine head. Thus these highly mobile spines were most likely filopodia. We observed such a new or lost filopodium in every second to third image. These are not enough data points for a size analysis or detailed description of filopodia turnover.

We added this observation to the results: We recorded STED images at different fields of view; each field of view was recorded at three time points at a time interval Δt of either 30 min (Fig. 3A, B), 60 min or 120 min. “Over these time periods the spines were mostly stable. Occasionally, a spine was lost or a new one appeared; none of these spines carried PSD95 and they were therefore most likely highly dynamic filopodia (Berry and Nedivi, 2017).”

[Editors' note: further revisions were suggested prior to acceptance, as described below.]

The manuscript has been significantly improved but there are some remaining issues, mostly concerning aspects of the biological context of the study, that need to be addressed, as outlined below:

We would like to thank the reviewer for the very helpful comments that improved the clarity of the manuscript. We have addressed all points raised below and hope the revised version of our manuscript is now suitable for publication.

Line 32-33: "… synaptic strength is set more precisely."

The term "precise" implies that there is a specific value around which the synaptic strength is set, which is not clear in this case. It is suggested that the authors use a more neutral expression such as ".. synaptic strength is set more uniformly" to describe the key observation.

We agree and changed the wording accordingly. (New line 28)

Line 62: "… which was confirmed recently (Holler et al., 2021)."

It would be informative to indicate that the study involved EM (CLEM) analysis, and should be rephrased, for example, "… which was confirmed recently at ultrastructural resolution…".

Thank you, we added this phrase. (New line 52)

Line 67: "… directly after LTP."

-> … directly after LTP induction.

Added. (Line 56)

Line 70: "… with a delay of ~1 hour after LTP"

As with above, potentiation itself can last for hours, and thus the timeframe being referred to needs to be clear.

-> e.g. … with a delay of ~1 hour after inducing LTP

This is indeed a valuable point. We added “inducing” (Line 58)

Line 76: "… of the spine and postsynapse at increased activity and baseline in vivo."

The increased activity is an assumption, and this should be reflected in the statement. For example, one could rephrase along the lines of "… of the spine and postsynapse in vivo using mice reared in enriched environment representing increased activity conditions and normal housing representing baseline conditions."

We agree that this is more specific and changed the sentence as suggested. (Line 63-65)

Line 123

Is it meant that in one possibility, EE could directly affect "pre-existing" synapses rather than "all" synapses?

In our experimental setting, we cannot distinguish between spines that were affected by EE and those that were not. However, with this sentence (New line 91-92) we wanted to emphasize that previous studies focused on the observation of spine stability, thus spine formation and elimination, but nothing is known about morphological changes of the spines and synapses. Such morphological changes could affect all spines – those newly formed due to the EE housing and preexisting. Of course, in the future it would be interesting to investigate whether the morphology of spines and synapses which are formed due to learning are different than the preexisting ones.

We rephrased as follows: “However, it is unknown whether the effects of EE leave their mark only at the level of spine formation and elimination such as observed by Yang et al. (Yang et al., 2009), or whether EE affects also the dynamics and nanostructure of all individual spines and PSDs.”

Line 190

In addition to the reference to Steffens et al., 2020, it would be helpful to the reader if the authors briefly mention what particular procedural features help ensure the craniotomy be atraumatic as possible.

With the phrase “as atraumatic as possible” (Line 155) we do not refer to a particular procedure. We want to emphasize that care needs to be taken at all surgical steps, the drilling, removal of the bony plate and removal of the dura to not damage the cortical surface. This may seem obvious, but in our experience, different experimenters perform a craniotomy very differently. This is much more critical for superresolution microscopy than for two-photon imaging but difficult to put into stringent operating instructions.

We added more details: “As described in detail in Steffens et al. (Steffens et al., 2020), critical steps involve a craniotomy that is as atraumatic as possible and does not damage the cortical surface when drilling or removing the bone plate and dura mater. Moreover, the gap between the brain surface and the cover glass needs to be negligible small and the right choice of the dental cement is important to avoid bending of the cover glass.”

Line 580: "To study activity-dependent changes, we compared adult mice housed in standard cages with age matched EE housed mice and analyzed synaptic morphology and plasticity …."

The motivation for the study is somewhat misguided and unclear, since the present work assessed the influence of activity by subjecting mice to activity-enhanced conditions by means of EE, and did not directly examine activity-dependent changes per se. Moreover, as acknowledged by the authors, there may be an issue also of anesthesia. The starting sentence as well as the rest of the discussion in the paragraph need to reflect these points.

We thank the reviewer for this comment. Indeed, we did not measure activity-dependent changes but changes due to the different rearing conditions – with and without enhanced activity.

We rephrased the first sentence accordingly (line 489): “To study whether different activity conditions during rearing influence synapse and spine size and plasticity, we compared adult mice housed in standard cages with age matched EE housed mice and analyzed synaptic morphology and plasticity in the visual cortex after the critical period.”

A large part of the paragraph deals mainly with spine and PSD95 assembly size differences between EE and Ctr housed mice. These sizes should not be influenced by the anesthesia and thus reflect differences between EE and Ctr housing as described.

At the end of the paragraph, where we discuss “the correlation between spine head and PSD95 changes”, we added a note about the influence of the anesthesia: “As discussed above, however, the dynamic might be influenced by the anesthesia and different in the awake state. (Line 511).”

It should be noted that both groups, EE and Ctr, were treated in the same manner and imaged under anesthesia. Therefore, we assume that the difference in plasticity is due to the different rearing /housing conditions with and without enhanced activity. However, the amplitude of those changes might indeed be different in the awake state.

Paragraph starting from Line 606

As with the comment above, the caveats of the present experimental design, including the effects of anesthesia, should be acknowledged.

In this paragraph, starting in new line 513, we discuss the dynamical changes and we agree with the above comment of the reviewer that it should be mentioned that these changes do not reflect activity dependent changes at the moment of the measurement but do reflect the plasticity after rearing under enhanced activity.

Thus, we added (Line 525): “However, it should be noted that our in vivo measurements were performed under anesthesia and directly after implanting a cranial window; therefore differences are due to the different rearing conditions and not to changes in activity at the moment of the measurement. The in vitro silencing, in contrast, continued over the measurements.”

https://doi.org/10.7554/eLife.73603.sa2

Article and author information

Author details

  1. Waja Wegner

    1. Optical Nanoscopy in Neuroscience, Center for Nanoscale Microscopy and Molecular Physiology of the Brain, University Medical Center Göttingen, Göttingen, Germany
    2. Max Planck Institute for Multidisciplinary Sciences, City Campus, Göttingen, Germany
    Contribution
    Conceptualization, Formal analysis, Investigation, Methodology, Resources, Visualization, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  2. Heinz Steffens

    1. Optical Nanoscopy in Neuroscience, Center for Nanoscale Microscopy and Molecular Physiology of the Brain, University Medical Center Göttingen, Göttingen, Germany
    2. Max Planck Institute for Multidisciplinary Sciences, City Campus, Göttingen, Germany
    Contribution
    Investigation, Methodology, Writing – review and editing
    Competing interests
    No competing interests declared
  3. Carola Gregor

    1. Department of NanoBiophotonics, Max Planck Institute for Multidisciplinary Sciences, Göttingen, Germany
    2. Department of Optical Nanoscopy, Institut für Nanophotonik Göttingen e.V., Göttingen, Germany
    3. Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany
    Contribution
    Resources, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
  4. Fred Wolf

    1. Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany
    2. Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
    3. Göttingen Campus Institute for Dynamics of Biological Networks, Göttingen, Germany
    Contribution
    Formal analysis, Methodology, Writing – original draft
    Competing interests
    No competing interests declared
  5. Katrin I Willig

    1. Optical Nanoscopy in Neuroscience, Center for Nanoscale Microscopy and Molecular Physiology of the Brain, University Medical Center Göttingen, Göttingen, Germany
    2. Max Planck Institute for Multidisciplinary Sciences, City Campus, Göttingen, Germany
    3. Cluster of Excellence "Multiscale Bioimaging: from Molecular Machines to Networks of Excitable Cells" (MBExC), University of Göttingen, Göttingen, Germany
    Contribution
    Conceptualization, Data curation, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing
    For correspondence
    kwillig@mpinat.mpg.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1860-334X

Funding

Deutsche Forschungsgemeinschaft (EXC171)

  • Waja Wegner
  • Heinz Steffens
  • Katrin I Willig

Deutsche Forschungsgemeinschaft (EXC 2067/1- 390729940)

  • Carola Gregor
  • Katrin I Willig
  • Fred Wolf

Max Planck Institute for Multidisciplinary Sciences (Open Access Funding)

  • Waja Wegner
  • Heinz Steffens
  • Carola Gregor
  • Katrin I Willig

VolkswagenStiftung (Göttingen Campus Institute for Dynamics of Biological Networks)

  • Fred Wolf

Deutsche Forschungsgemeinschaft (CRC 889)

  • Fred Wolf

Deutsche Forschungsgemeinschaft (CRC 1286)

  • Fred Wolf

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Dr Siegrid Löwel (Göttingen University) for suggesting and providing the Marlau cages, the animal facility of the MPI for Multidisciplinary Sciences, City Campus, for excellent support, Dr Karl Deisseroth and Dr Don Arnold for providing plasmids and Jaydev Jethwa for critical reading. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) within the DFG Research Center and Cluster of Excellence (EXC 171, Area A1) 'Nanoscale Microscopy and Molecular Physiology of the Brain' (WW, HS, KIW) and under Germany’s Excellence Strategy – EXC 2067/1-390729940 (KIW, CG, FW). This work was supported by the Niedersächsisches Vorab through the Göttingen Campus Institute for Dynamics of Biological Networks (FW), and by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) through CRC 889, CRC 1286 and the PP 2205 "Evolutionary Optimization of Neuronal Processing" (FW).

Ethics

Experiments were performed according to the guidelines of the national law regarding animal protection procedures and were approved by the responsible authorities, the Niedersächsisches Landesamt für Verbraucherschutz (LAVES, identification number 33.9-42502-04-14/1463). All surgery and imaging was performed under anesthesia, and all efforts were made to minimize animal suffering and the number of animals used.

Senior Editor

  1. Lu Chen, Stanford University, United States

Reviewing Editor

  1. Yukiko Goda, RIKEN, Japan

Publication history

  1. Preprint posted: October 23, 2020 (view preprint)
  2. Received: September 3, 2021
  3. Accepted: February 22, 2022
  4. Accepted Manuscript published: February 23, 2022 (version 1)
  5. Version of Record published: March 8, 2022 (version 2)

Copyright

© 2022, Wegner et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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  1. Waja Wegner
  2. Heinz Steffens
  3. Carola Gregor
  4. Fred Wolf
  5. Katrin I Willig
(2022)
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https://doi.org/10.7554/eLife.73603
  1. Further reading

Further reading

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    Neural circuit formation and function require that diverse neurons are specified in appropriate numbers. Known strategies for controlling neuronal numbers involve regulating either cell proliferation or survival. We used the Drosophila visual system to probe how neuronal numbers are set. Photoreceptors from the eye-disc induce their target field, the lamina, such that for every unit eye there is a corresponding lamina unit (column). Although each column initially contains ~6 post-mitotic lamina precursors, only 5 differentiate into neurons, called L1-L5; the ‘extra’ precursor, which is invariantly positioned above the L5 neuron in each column, undergoes apoptosis. Here, we showed that a glial population called the outer chiasm giant glia (xgO), which resides below the lamina, secretes multiple ligands to induce L5 differentiation in response to epidermal growth factor (EGF) from photoreceptors. By forcing neuronal differentiation in the lamina, we uncovered that though fated to die, the ‘extra’ precursor is specified as an L5. Therefore, two precursors are specified as L5s but only one differentiates during normal development. We found that the row of precursors nearest to xgO differentiate into L5s and, in turn, antagonise differentiation signalling to prevent the ‘extra’ precursors from differentiating, resulting in their death. Thus, an intricate interplay of glial signals and feedback from differentiating neurons defines an invariant and stereotyped pattern of neuronal differentiation and programmed cell death to ensure that lamina columns each contain exactly one L5 neuron.

    1. Neuroscience
    Brittany J Bush, Caroline Donnay ... J Christopher Ehlen
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    Resilience, the ability to overcome stressful conditions, is found in most mammals and varies significantly among individuals. A lack of resilience can lead to the development of neuropsychiatric and sleep disorders, often within the same individual. Despite extensive research into the brain mechanisms causing maladaptive behavioral-responses to stress, it is not clear why some individuals exhibit resilience. To examine if sleep has a determinative role in maladaptive behavioral-response to social stress, we investigated individual variations in resilience using a social-defeat model for male mice. Our results reveal a direct, causal relationship between sleep amount and resilience-demonstrating that sleep increases after social-defeat stress only occur in resilient mice. Further, we found that within the prefrontal cortex, a regulator of maladaptive responses to stress, pre-existing differences in sleep regulation predict resilience. Overall, these results demonstrate that increased NREM sleep, mediated cortically, is an active response to social-defeat stress that plays a determinative role in promoting resilience. They also show that differences in resilience are strongly correlated with inter-individual variability in sleep regulation.