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Altered Synapse Stability in the Early Stages of Tauopathy

Summary Synapse loss is a key feature of dementia, but it is unclear whether synaptic dysfunction precedes degenerative phases of the disease. Here, we show that even before any decrease in synapse density, there is abnormal turnover of cortical axonal boutons and dendritic spines in a mouse model of tauopathy-associated dementia. Strikingly, tauopathy drives a mismatch in synapse turnover; postsynaptic spines turn over more rapidly, whereas presynaptic boutons are stabilized. This imbalance between pre- and post-synaptic stability coincides with reduced synaptically driven neuronal activity in pre-degenerative stages of the disease.


Supplementary Experimental procedures
Surgery, imaging and image analysis were performed blind with respect to genotype and age.

Animals
Adult male mice of the rTg(tau301L)4510 (TG; n=14) line and wild-type littermate controls (WT;n=16) were used between 18 and 29 weeks of age . Mice were screened for activator and responder transgenes using a standardized PCR assay . Although this transgene contains a conditional expression cassette, mice were maintained off doxycycline-containing diet throughout, meaning mutant tau was continually expressed . All mice were given ad libitum access to food and water and maintained in a 12 hour light-dark cycle. All procedures were conducted by researchers holding a UK personal licence and conducted in accordance with the UK Animals (Scientific Procedures) Act 1986 and subject to internal ethical review.

Surgery
Cranial windows were surgically implanted over the somatosensory cortex as previously described (Holtmaat et al., 2012). In addition, adeno-associated virus expressing GFP or GCaMP6m was injected into the layer 2/3 of the cerebral cortex to enable the visualisation of cortical neurons. Briefly, mice were anaesthetized with isofluorane and administered dexamethasone (30 mg/kg) to limit brain swelling and the analgesic, buprenorphine (5 mg/kg) pre-operatively. The skull was exposed and a 5 mm diameter craniotomy drilled over the somatosensory cortex. For neurite imaging experiments AAV serotype 2 expressing GFP (10 10 GU; Vector Biolabs), or, for neuronal activity imaging experiments, AAV (1/2 hybrid serotype) containing GCaMP6m (Chen et al., 2013) under control of the human synapsin promoter (UPenn Vector Core) was injected into layer 2/3 of the cerebral cortex (3 injection sites, 0.3µl per site, 300µm below the dura). A glass coverslip was placed over the craniotomy and sealed with glue and dental cement. A screw was placed in the skull on the contralateral side for added stability. The whole skull was subsequently covered in dental cement and a stainless steel head-post (<500 mg) was affixed to the skull contralateral to the cranial window using bone cement for positioning on the two-photon microscope. Mice were allowed to recover for at least 3 weeks before imaging began. Pilot studies showed that the injected virus diffused in the cortex and transduced cells in all cortical layers in both groups of animals.

Immunohistochemistry
Brains were coronally dissected into three segments using an adult mouse brain matrix (slot #5 and #11 AP; RBM-2000C: ASI Instruments, USA). These segments were processed using the Tissue TEK® VIP processor (GMI Inc, USA) and embedded in paraffin wax. The middle segment was used to cut 8 μm serial sections using rotary microtomes (HM 200 and HM 355; Thermo Scientific, Germany) which were mounted on glass slides. Coronal sections representing approximately Bregma -1.50 (AP) were selected for immunohistochemistry using primary antibodies specific for phospho-tau (PG-5, tau phosphorylated at s409; 1:8000, from Peter Davies) and microglia (Iba-1: 1:6000, Wako Chemicals GmbH, Germany). Following de-paraffinisation and rehydration of the tissue, antigen retrieval was performed using the Lab Vision PT module system (Thermo Scientific,  Sections were then counterstained with haematoxylin before dehydration and cover-slipping.

Neuropathological analysis
Stained sections were digitised using the Scanscope XT slide scanner (Aperio, USA) at 20× magnification. Imagescope software (version 11.1.2.760; Aperio, USA) was used to view the digitised tissue sections and delineate boundaries of the hippocampus and overlying cortex (including barrel field of the somatosensory cortex) for both the right (cranial window) and left (contralateral) side of brain. Immunoreactivity for PG-5 positive tau pathology and Iba-1 positive microglia within the regions of interest was quantified using the positive pixel algorithm (Imagescope, version 11.1.2.760; Aperio, CA, USA) and expressed as a percentage of the total area. Levels of Iba-1 and PG-5 in cortical regions were normalized to their respective levels in the hippocampus. Injection and cranial window implantation did trigger an increase in microglial activation, measured by Iba-1 immunostaining, in the ipsilateral cortex.
However, this both WT and rTg4510 mice were equally prone to this elevation ( Figure S1).
There was no difference in tau burden in cortex ipsilateral to the cranial window compared to the contralateral hemisphere ( Figure S1).

Two-photon imaging of neurite structure
Three groups of animals were imaged; an early group imaged between 18 and 19 weeks of age, a middle group imaged between 21 and 25 weeks, and a later group imaged between 25 and 29 weeks. All time points are prior to the onset of widespread cell death in the cortex.
A two-photon microscope (Prairie Technologies) equipped with a tuneable Ti:Sapphire pulsed laser (~100 fs pulsewidth, MaiTai HP, SpectraPhysics) and PrairieView acquisition software was used for all imaging experiments. Mice were anaesthetized with 3-5% isoflurane and secured to the microscope with the metal bar attached to the skull to a custom-built fixed support. Body temperature of 35-37 o C was maintained by a heating blanket and rectal probe.
A 10x objective (NA=0.3, Olympus) was used to identify characteristic blood vessels to reliably locate regions-of-interest (ROIs) at each imaging time point. A 40x water immersion objective (LUMPlanFI/IR, NA=0.8, Olympus) was used to acquire several ROI stacks (75 m x 75 m, 512 x 512 pixels, step size = 0.5 m) per animal. Excitation power at 910 nm was typically 35mW at the sample. GFP emission was collected through a 525/25nm filter. At the first imaging session, 5-10 ROIs of axons and/or dendrites were acquired ensuring that the ROIs were at least 100 m apart. Animals underwent subsequent imaging sessions, each lasting approximately one hour, up to a maximum of 20 sessions. The signal-to-noise ratio was measured in WT and rTg4510 images and no significant difference was found.
Neuronally-expressed GCaMP6m was excited by illuminating the cortical surface with 480 nm light and epifluorescence images were captured through a 500-550nm bandpass emission filter at 10 Hz using a 4x objective (Plan N, Olympus, NA 0.1) and 12-bit CCD camera (QICAM Fast 1394, QImaging). Stimulation-evoked responses were calculated per pixel per trial (10 trials, 20 s inter-trial interval) as the fractional change in mean GCaMP6m fluorescence 0-2 s post whisker-stimulation onset relative a 0-2 s pre-stimulation baseline. Response fields were determined as the largest contiguous area in which the in which the response amplitude exceeded 50% of the maximum response. Regions within the response field were subsequently targeted for higher magnification two-photon imaging.
GCaMP6m was excited at 910 nm and fluorescence collected using a 525/25 nm emission filter. Excitation power was <35mW at the sample Images were acquired using bidirectional frame scanning at 8 Hz, 256 x 128 pixel resolution, using ScanImage 3.8 software (Pologruto et al., 2003). Superficial cortical blood vessels were used as fiducial markers to guide imaging to regions as close as possible to the principal whisker response field that contained a high density of GCaMP6m expressing layer 2/3 neurons (100-300 µm below pial surface) and did not overlap with major blood vessels. A high-resolution (0.15 µm per pixel) reference image was acquired of each imaging field to assist post-hoc definition of neuronal regions of interest (ROIs). Spontaneous GCaMP6m signals were recorded continuously for 520 s. For whisker stimulation experiments, images were acquired continuously while the principal whisker was deflected caudo-rostrally (5° deflections, 10 Hz, 1 s) at a 20 s inter-stimulus interval across 25 trials using a piezo actuator. To ensure that a consistent depth of anaesthesia was maintained throughout and between experiments, breathing rate was continuously monitored using a piezoelectric wafer beneath the thorax, and anaesthesia adjusted to maintain a rate >100 breaths per min (Figure S2B-D) (Golshani and Portera-Cailliau, 2008). In separate experiments, we determined that small variations within the range of breathing rates used here (100-140 breaths per min) did not correlate with the rate or amplitude of spontaneous neuronal activity. Deeper anaesthesia, and associated slower breathing rate, dramatically reduced apparent excitability (data not shown).

Spine and bouton structure analysis
In vivo two-photon images were converted into stacks with ImageJ and the StackReg plugin

Neuronal activity analysis
Analyses were performed using Matlab (Mathworks) and ImageJ software (National Institutes of Health). Mechanical drift was corrected by registration to the average of the first 5 seconds (40 frames) using the TurboReg plugin (Thevenaz et al., 1998) in ImageJ. Summed and standard deviation projections of time-lapse images were used to manually draw ROIs around visually identified neuronal cell bodies.
For analysis of spontaneous GCaMP6m signals, neuronal fluorescence time series, F(t), were constructed by averaging the ROI pixels in each frame. The background time series was determined from a reference blood vessel and was subtracted from F(t). Relative time series were generated following the procedure of Jia et al. (Jia et al., 2011). Briefly, a time-varying baseline, F0(t), was determined by first smoothing F(t) with a ±1 s moving average filter to generate Fꞌ(t), and then for each time point, t, F0 was calculated as the minimum of Fꞌ(t) in the preceding 5 second epoch, such that: The relative fluorescence time series was then calculated as: Finally, the relative time series was smoothed with an exponentially weighted moving average to generate ΔF/F: This routine is robust against slow drifts in baseline fluorescence (Jia et al., 2011).
Spontaneous fluorescence transients were isolated using a high-throughput template-based event detection algorithm, following the methodology of Clements and Bekkers (Clements and Bekkers, 1997). A template, T, was constructed by averaging and smoothing (0.5 s moving average) the waveforms of 310 manually approved events (aligned to event onset) from WT neurons (n=3 mice, 5 sessions). The minima and maxima of T were normalised to 0 and 1 respectively. For each ΔF/F time series, T was advanced point-by-point along the time series and the fit, Q, calculated as: where T is the template, S is the template scaling factor, and P is the offset: where N is the number of samples in the template, Σ is the sum from 1 to N, and D is the period of the ΔF/F time series to fit, such that: The detection criterion, C, at each time point, t, was then calculated from S and the standard error of the fit, E: This algorithm directly relates the detection criterion to the signal-to-noise ratio of the fit, thus accounting for variable levels of noise within and between neuronal time series (Clements and Bekkers, 1997). Events were detected at times, t, when C(t) exceeded a threshold of 2. Event rise times were calculated using the 20-80 % portion of the initial rising phase of the event. Comparison of the signal-to-noise ratio of image regions from individual cells (calculated following the protocol of Kerlin et al. (2010)) showed that there was no difference in the population distribution between WT and rTg4510 animals ( Figure S2A). This suggests that the imaging preparation and GCaMP6m expression are comparable across genotype.
Statistical testing was performed using SPSS (IBM) and Matlab. Datasets were tested for normality and parametric or nonparametric tests used as appropriate with an α level of 0.05.
Data are presented as mean ± SEM unless otherwise stated. n numbers for statistical testing are noted as "n=…" throughout.

Statistical analysis of effects of individual animals
In all statistical analyses (except Figures 2C&F, where spine and TB results are compared within animals), the cell (or cellular events in Figure 3D) was chosen as our primary dependent variable (or "n" number). This level of analysis was used (in particular, avoiding averaging within each animal) because we were particularly interested in the variability between individual cells or neurites. This is because neurodegeneration is a slowly progressing process that affects different neurons at different times. It is changes in the characteristics of this population distribution that are likely to be a primary indicator of potential disease effects.
However, cells within an individual animal are not independent and may have a tendency to co-vary, which could generate bias within our datasets. To minimize potential difficulties due to unevenly weighted contributions of individual animals, balanced numbers of cells/neurites from each animal were included in each sample. Further, to formally test the effect of the animal on our datasets, Generalized Linear Mixed Models (GLMM, SPSS) were generated that include "Genotype" as a Fixed factor and "Animal" as a Random factor, allowing by-neuron random intercept, and by-animal random slope and by-genotype random slope. When repeated measures were made (eg, in longitudinal spine density measures), "Age" was also included as a Fixed factor, using a first-order autoregressive (AR1) covariance structure because we anticipate higher covariance between ages that are closer in time. Axon/spine density ( Figure   1) and cellular activity (Figure 3) values were modelled in the GLMM with a gamma distribution and log link function, since population distributions tended to be skewed to higher values.
Results of GLMMs of our structural (spine and TB density) and functional (spontaneous activity) datasets are presented in Figure S3. The effects of genotype in the GLMM are established by the "genotype coefficient" (Figure S3A), which indicates the average effect of being Tg4510-positive on spine or TB density. For example, Tg4510 dendrites in the late age group will, on average, have 0.594 spines/m -1 less than WT counterparts ( Figure S3A). The variance in the GLMM that is due to animal-related effects is measured by the "variance due to animal" metric. Significance values for these predicted effects are shown in the final two columns of Figure S3A (green highlights p<0.05). In line with analyses presented in Figure 1, significant decreases due to the transgene are found in spine density in the mid and late age groups ( Figure S3B) and in TB density in the late group ( Figure S3C). In none of the datasets was there a significant effect of within-animal co-variance (Figure S3A-C). Similar GLMMs were used to analyse the spontaneous neuronal calcium elevations recorded in vivo (shown in Figure 3A-D). In these analyses, being Tg4510-positive is associated with a significant reduction in the frequency, ampltiude and rise time of spontaneous events (Figure S3D-G).
Again, in these data, the effect of individual animal identity had no significant influence on the GLMM predictions (Figure S3D-G). This quantitatively defines the contribution of animal-toanimal variability as having a limited impact on our findings. We were not able to obtain wellfitted GLMMs for the binomial data for response to whisker stimulation ( Figure 3E-F