Tau Pathology Induces Excitatory Neuron Loss, Grid Cell Dysfunction and Spatial Memory Deficits Reminiscent of Early Alzheimer's Disease

Keywords: Tau pathology, Alzheimer's disease, entorhinal cortex, grid cells, neuronal loss, cognitive deficits, transgenic mouse model

Mature Tau Pathology is Associated with Spatial Memory Deficits in Aged EC-Tau Mice

We have previously shown that at younger ages (≤ 14 months), human tau in the EC-tau mouse line was diffuse and mainly distributed in axons with a few cells in the EC and parasubiculum (PaS) showing accumulation in the somatodendritic compartment (Liu et al., 2012). By 30+ mo of age, axonal staining in EC-Tau mice was almost completely lost, and human tau protein had accumulated extensively in cell bodies within the hippocampal and parahippocampal regions, as well as in extrahippocampal areas of the cortex (Figure 1A). There was negligible somatodendritic human tau staining in control mice (Figure S1A). To investigate whether mature tau pathology in aged mice could induce learning and memory deficits we compared EC-Tau and control mice at 14 and 30+ months of age by Morris water maze (MWM) and T-maze tests. At 14 months of age, there were no significant learning and memory deficits in EC-Tau mice compared to matched controls (Figures 1B-1E), which is consistent with published data that showed EC-Tau mice at 16 months of age were cognitively normal (Harris et al., 2012; Polydoro et al., 2014). At 30+ months of age, however, there were significant differences between EC-Tau and control mice in escape latency (P < 0.05, 0.01 and 0.001 for Trial day 2-4, respectively) (Figure 1B), the number of platform crossings in the 2 h (t = 3.693, df = 12, P = 0.0031) (Figure 1C) and 24 h probe trial of MWM (t = 3.083, df = 12, P = 0.0095) (Figure 1D), and the percent correct choice in T-maze (Chi-square = 4.822, df = 1, P = 0.0281) (Figure 1E). In addition, there were significant differences in escape latency (P < 0.001 and P < 0.05 for Trial day 2 and 3, respectively) (Figure 1B) and the number of platform crossings in the 2 h (t = 4.393, df = 15, P = 0.0005) (Figure 1C) and 24 h probe trial of MWM (t = 4.954, df = 15, P = 0.0002) between control mice at 14 mo and control mice at 30+ mice (Figure 1D), with a trend to decrease that did not reach significance in the percent of correct choice in the T-maze (Chi-square = 0.8046, df = 1, P = 0.3697) in 30+ mo control mice (Figure 1E). However, the differences in escape latency (P < 0.001 for Trial day 2-4), the number of platform crossings in the 2 h (t = 7.085, df = 14, P < 0.0001) and 24 h probe trial of MWM (t = 4.736, df = 14, P = 0.0003), and the percent of correct choice in the T-maze (Chi-square = 6.513, df = 1, P = 0.0107) between EC-Tau mice at 14 mo and EC-Tau mice at 30+ mo were greater than the differences between controls at 14 and 30+ mo. Moreover, there was no significant difference in escape latency on Trial day 4 between control mice at 14 mo and control mice at 30+ mo, but a significant difference between EC-Tau mice at 14 mo and EC-Tau mice at 30+ mo (P < 0.001). Swim speed, visual ability and body weight were not significantly different between experimental groups at 14 and 30+ months of age (Figures S1B-1D), which suggests that the learning and memory deficits in aged EC-Tau mice were not attributable to abnormalities in sensorimotor function, visual acuity or body weight.

Reduced Grid Cell Firing and Periodicity in the Dorsal MEC of Aged EC-Tau Mice

As the MEC is known to be involved in spatial learning and memory, we wanted to test whether tau pathology impacts the underlying physiology of MEC neurons. Multi-electrode in vivo electrophysiology was used to extracellularly record from neurons in the MEC of 14 mo and 30+ mo EC-Tau mice and age-matched controls (Figure 2). The animals were surgically implanted with 16-channel electrodes aimed at dorsal MEC (coordinates: 3.1mm lateral to lambda, 0.2mm in front of transverse sinus, angled at 6-8° posterior and 0.8mm below the surface of the brain. See Method Details of STAR Methods, “Electrode implantation and surgery”) (Figure S3C-D). The animals were then allowed to explore one of several open field arenas each day for two sessions as we recorded single unit and local field potentials (LFPs). Electrodes were turned no more than 50 μm each day to maintain stable recordings, and the electrophysiological data were analyzed to identify neuronal firing patterns and network activity (see Figure S2 and STAR Methods for Method details). A total of 434 excitatory neurons were recorded in 29 mice (for cell-type information see Method Details of STAR Methods, “In vivo recording and cell classification”). Grid cells were identified based on their grid-like firing patterns, and their spatial periodicity was calculated into a grid score for each cell. Grid scores were calculated for all cells and only those with positive grid scores were shuffled randomly to obtain 122 grid cells that exceeded the 95th percentile threshold (Figure S3A). At 14 mo, we observed no difference in grid field patterns from EC-Tau mice compared to age-matched controls (Figure 2A). However, 30+ mo EC-Tau mice showed poor specificity of grid field patterns compared to other groups (Figure 2B). We observed reduced grid scores in 30+ mo EC-Tau mice compared to age matched controls (Figure 2C, left panel; P < 0.0001, Mann-Whitney U: 143.5) and histograms (30 bins) of grid score percentages showed clear separation of the 30+ mo EC-tau group from other groups (Figure 2C, right panel). Grid cells in the 30+ mo EC-Tau mice also exhibited reduced peak firing (P < 0.0001, Mann-Whitney U: 127.5) and average firing (P = 0.0161, Mann-Whitney U: 243.0) rates compared to age-matched controls (Figure 2D). In addition, the grid cells of 30+ mo EC-tau mice had reduced information content, measured in bits per second, compared to the aged controls (Fig. 3A, left panel; P < 0.0001, Mann-Whitney U: 146.5). We did not detect differences in grid cell information content between the 14-mo groups. There were no differences in spatial coherence of grid cells between age-matched groups (Fig. 3A, right panel). We also analyzed firing rates of other MEC cell types, including head-direction cells, border cells, and non-classified cells, but did not detect any differences in peak or average firing rates between age-matched EC-Tau mice and controls (Figure S5).

Increased Interneuron Firing and Enhanced Power of Theta in the Dorsal MEC of Aged EC-Tau Mice

MEC interneurons are intricately connected to stellate and pyramidal cells, including grid cells. Thus, we examined whether firing properties of MEC interneurons were also altered in aged EC-Tau mice as a result of tau pathology. A total of 93 interneurons were recorded in 29 mice (for cell-type information see Method Details of STAR Methods, “In vivo recording and cell classification”). There were no significant differences in the proportion of interneurons recorded with respect to other cell-types in any age group (P > 0.05, see STAR Methods). Putative interneurons in the MEC were identified based on their narrow spike widths and higher firing rates compared to wide spiking cells (Quirk et al., 2009). We found the average firing rate of interneurons was increased in 30+ mo EC-Tau mice compared to age-matched controls (P = 0.0193, Mann-Whitney U: 142.5), with no effect between groups at 14-mo (Figure 3B). Peak firing rate was not affected in either group at any age. To investigate if the overall network activity in MEC was affected, local field potentials were also recorded and analyzed. We found an increase in power distribution in the theta frequency range (4-12 Hz) in 30+ mo EC-Tau mice compared to controls (Figures 3C-E, two-way ANOVA. df = 717 Interaction: P < 0.0001 F = 18.98, Genotype: P < 0.0001 F = 25.14 and Age: P = 0.0323 F = 4.600). There was no significant difference in power distribution in the gamma frequency range (35-55 Hz) for any groups. We found no differences between groups at any age in theta- or gamma-phase locked interneurons (data not shown).

Excitatory but not Inhibitory Neurons are Vulnerable in Aged EC-Tau Mice

Significant neuronal loss was previously reported at 24 months of age (but not at 21 months of age) in the EC-II and PaS of EC-Tau mice compared to control mice (de Calignon et al., 2012). Consistent with this finding, we also found that the number of neurons (NeuN⁺) in MEC-II (P = 0.0079) as well as MEC-III/IV (P = 0.0018) was significantly reduced in 30+ mo EC-Tau mice compared to control mice (Figure S4A). Thioflavin S⁺ tau, indicative of mature tangles, was only observed in the EC and pre/parasubiculum (Figures S4B-4D) at 24 and 34 mo, with larger aggregates being observed at 34 mo. AT100 (a marker of AD-related hyperphosphorylated tau (Matsuo et al., 1994) labeled a larger set of neurons than Thioflavin S at younger ages but co-localized with Thioflavin S⁺ tau at 30+ mo (Figures S4B-4D). To identify which type of cell accumulated pathological human tau, we performed sequential staining with antibodies against human tau (MC1⁺), excitatory neurons (TBR1⁺) and inhibitory neurons (PV⁺ or SOM⁺) (Belforte et al., 2010) in 30+ mo EC-Tau and control mice (Figure 4). The MC1⁺-tau staining in the MEC was completely colocalized with TBR1⁺ excitatory neurons, but not with PV⁺ or SOM⁺ inhibitory neurons (Figures 4A-4C, and 4E). Furthermore, the number of TBR1⁺ excitatory neurons was significantly reduced in the MEC-II and MEC-III/IV in aged EC-Tau mice compared to matched controls (P = 0.0022) (Figure 4D). However, there was no difference in the number of PV⁺ or SOM⁺ inhibitory neurons between aged EC-Tau and control mice (P > 0.05) (Figure 4D).

Immunohistochemistry

Mice were transcardially perfused with phosphate-buffered saline (PBS) (Fisher Scientific, Fair Lawn, New Jersey, USA), brains were harvested and drop-fixed in 4% paraformaldehyde (PFA) (Electron Microscopy Sciences, Hatfield, PA, USA) in PBS at 4^oC overnight, followed by incubation in 30% sucrose (Sigma-Aldrich, Saint Louis, MO, USA) in PBS until the brains sank to the bottom of the container. Brains were sectioned (35 μm) throughout on a horizontal plane with a cryostat (Leica CM3050S, Leica Biosystems, Buffalo Grove, IL, USA), and collected in individual wells. Representative free-floating sections from EC-Tau and littermate control mice at 30+ month of age were stained with human specific, anti-tau antibodies MC1 or CP27 (courtesy of Dr. Peter Davies; 1:500), mouse/human AT8 or AT180 (Thermo Scientific, Rockford, IL, USA; 1:500). In addition, every 9th free-floating sections from 30+ mo EC-Tau and control mice (n = 5 mice per group, 10 sections each mouse) starting from Bregma -2.04 mm to Bregma -4.88 mm were selected and stained with mouse NeuN antibody (EMD Millipore, Billerica, MA, USA; 1:1000), which is a specific neuronal marker.

Immunostaining was performed as previously described (Liu et al., 2012; Khan et al., 2014). In brief, free-floating mouse brain sections were blocked in 1.5% H₂O₂ (Fisher Scientific) in PBS for 10 min, and incubated with the above appropriate primary antibody in PBS containing 0.3% Triton X-100 (Sigma-Aldrich) (PBST) and 5% normal goat serum (Vector Laboratories, Burlingame, CA, USA) at 4°C overnight. After three washes with PBST, the sections were incubated with SuperPicture horseradish peroxidase (HRP) polymer conjugate Broad spectrum (Invitrogen, Frederick, MD, USA) for 15 min at room temperature on a rotator. Following three washes with PBS, immunoreactive material was visualized using 3,3′-diaminobenzidine tetrahydrochloride (DAB) (Sigma-Aldrich) as chromagen. The stained sections were mounted on slides and inspected by light microscopy (Olympus BX50, Olympus, Center Valley, PA, USA).

A semiquantitative count of NeuN-positive neurons in the EC-II and EC-III/IV was performed in selected sections from 5 control and 5 EC-Tau mice. For each mouse, a total of 10 NeuN-stained horizontal sections starting from Bregma -2.04 mm, spaced at ∼300 μm, were included for automated cell counting (http://imagej.net/Particle_Analysis) using the ImageJ software (version 1.48, US National Institutes of Health, Bethesda, Maryland, USA).

Morris water maze (MWM) tests

Spatial learning and memory was assessed using the MWM test including hidden platform acquisition and probe trial test. All the tests were automated and recorded using ANY-maze video tracking software (Stoelting Co., Wood Dale, IL, USA). Briefly, mice (n = 10 controls and 9 EC-Tau mice at 14 mo; n = 7 controls and 7 EC-Tau mice at 30+ mo) were tested in a cylindrical tank of 120 cm in diameter and 60 cm in height. The tank was filled with water at around 25 °C made opaque by adding Palmer Prism tempera white paint (Palmer Paint Products, Troy, Michigan, USA) until the platform was submerged 1.5 cm below the water surface. The tank was divided into four quadrants with different navigation landmarks for each quadrant. The midpoint of the wall in each quadrant was used as the starting location from where animals were released into the water.

In the hidden platform acquisition test, mice were allowed to swim freely to search for the escape platform within a maximum of 60 s. The platform location remained constant throughout the test. The time taken to reach the platform was recorded as the escape latency. The mouse was kept on the platform for 10 s after it found the hidden platform. If a mouse failed to find the platform within 60 s, it was guided to the platform and placed on the platform for 10 s; in this case, the escape latency was recorded as 60 s for this trial. The same animal was released from a new starting quadrant 4 min after the previous trial. The experiment was repeated with 6 trials per mouse each day and 4 consecutive days. The mean escape latency was measured to evaluate the spatial learning ability.

Two hours and twenty-four hours after the hidden platform acquisition test, probe trials were conducted by removing the platform. Mice were placed in the diagonal quadrant of the hidden platform originally located and were allowed to swim freely in the pool for 60 s. The number of entries into the area around the original hidden platform, i.e. the number of crossings over the previous platform location, was used to indicate the short-term and long-term memory maintenance.

At the end of the 24 h probe trial, a colorful flag was placed on the top of the hidden platform, which was opposite to the testing quadrant. The mouse was released at three different quadrants, and the time spent to find the flag was recorded as a measure of visual ability. All of the MWM tests were performed by two experienced technicians who were blind to the genotype of the animals.

T-maze spontaneous alternation

Mice (n = 10 controls and 9 EC-Tau mice at 14 mo; n = 8 controls and 8 EC-Tau mice at 30 mo) were tested for spontaneous alternation on a T-shaped maze as previously described (Deacon and Rawlins, 2006). We used a spatial T-maze which had a black cue card on the left side of the stem. After one-day habituation, mice were first placed at the stem of the T-maze [measured 40 (stem) × 46 (arm) × 10 (width) cm] and allowed to choose to explore one open goal arm of the maze for 30 s throughout a twelve-trial continuous alternation sessions. Once a goal arm was chosen, the mouse was placed in the stem of the maze again, facing away from the goal arms, and the trial was repeated. The inside of the T-maze was wiped with 70% ethanol between different animal trials. For each animal, number of correct and incorrect choices were recorded in a contingency table. At the end of the experiment chi-square test was done to test statistical significance. All the tests were recorded by a technician blind to the genotype of the animals.

Electrode implantation and surgery

Custom-made, reusable 16-channel microdrives (Axona, UK) were constructed as described previously by attaching an inner (23 ga) and an outer (19 ga) stainless steel cannula to the microdrives (Hussaini et al., 2011). Tetrodes were built by twisting four 25 mm thick platinum-iridium wires (California wires) and heat bonding them. Four such tetrodes were inserted into the inner cannula of the microdrive and connected to the wires of the microdrive. One day prior to surgery, the tetrodes were cut to an appropriate length and plated with a platinum/gold solution until the impedance dropped to about 150 ohms. On the day of surgery, mice were anesthetized with a mixture of ketamine and xylazine (100 mg/ml and 15 mg/ml, respectively, per 10 g body weight) and monitored for depth of anesthesia before proceeding. Mice were then fixed within the stereotaxic frame with the use of zygomatic process cuff holders and an incision was made to expose the skull. About 3–4 jeweler's screws were inserted into the skull to support the microdrive implant. An additional screw connected with wire was also inserted into the skull which served as a ground/reference for local field potential (LFP) recordings. A 2 mm hole was made on the skull at position 3.1 mm lateral to the midline and ∼0.2 mm in front of the transverse sinus. Tetrodes were then lowered to about 0.8 mm from the surface of the brain (below dura) at an angle of 6-8^o in the posterior direction in the sagittal plane. Dental cement was spread across the exposed skull and secured with the microdrive. Mice were allowed to recover from anesthesia in a cleaned cage placed on a warm heating pad until awake (∼45 min) before finally being transported to housing. Carprofen (5 mg/kg) was administered to mice prior to surgery and post-operatively to reduce pain. Mice usually recovered within 24 h, after which the tetrodes were lowered and recording began. All recording depths reported are from the surface of the brain.

In vivo recording and cell classification

The mice explored four alternating arenas during recording sessions: three white boxes (Box 1: 45 × 45 cm; Box 2: 55 × 55 cm; Box 3: 70 × 70 cm) or a white cylinder (dia. 70 cm). Twenty nine mice underwent two recording sessions per day, with ≥4 hr between sessions. For each new recording session, mice were exposed to either 1) a different arena than the previous session, or 2) the same arena as the previous session, but with the arena and visual cue rotated. Tetrode positions were moved not more than 50μm at a time, and only after the last recording session of the day, allowing >12 hr of stable electrode positioning prior to the next recording session. All mice included in our study underwent ≥16 recording sessions, with the 30+ mo EC-Tau group having the highest number of sessions (1.3× more than age-matched control) to obtain similar overall cell numbers. Neuronal signals from experimental mice were recorded using the Axona DacqUSB system. The signals were amplified 15,000 to 30,000 times and band pass filtered between 0.8 and 6.7 kHz. LFP was recorded from 4 channels of the electrodes. The LFP was amplified 15,000 times, lowpass filtered at 125 Hz and sampled at 250 Hz. Notch filter was used to eliminate 60 Hz noise. The recording system tracked the position of the infrared LED on the head stage (sampling rate 50 Hz) by means of an overhead video camera. Position data were speed-filtered, only speeds of 3 cm/s or more were included. Tracking artifacts were removed by deleting samples greater than 100 cm/s and missing positions were interpolated with total durations less than 1 s, and then smoothing the path with a 21- sample boxcar window filter (400 ms; 10 samples on each side). Spike sorting was performed offline using TINT cluster-cutting software and Klustakwik automated clustering tool (Figure S2). The resulting clusters were further refined manually and were validated using autocorrelation and cross-correlation functions as additional separation tools. Quantitative measurements of cluster quality were subsequently performed, yielding isolation Distances in Mahalanobis space (Schmitzer-Torbert et al., 2005). There were no significant differences between clusters in our experimental groups (Median Isolation distances: 30+ mo EC-Tau- 10.40, 30+ mo control- 10.10, 14-mo EC-Tau- 9.46 and 14-mo control- 10.50. Kruskal-Wallis test- P = 0.334). A total of 434 excitatory neurons (122 grid cells, 93 head-direction (HD) cells, 22 border cells and 197 non-classified (NC) cells) and 93 interneurons were recorded from 29 mice for this study. There were four groups of mice and cell types for each group is as follows. Six 30+ mo EC-tau mice (26 grid; 26 HD; 5 border; 36 NC cells; and 22 interneurons), six 30+ mo control mice (30 grid; 15 HD; 7; border; 40 NC cells; and 22 interneurons), nine 14-mo old EC-tau mice (36 grid; 32 HD; 6 border; 78 NC cells; and 22 interneurons) and eight 14-mo old control mice (30 grid; 20 HD; 4 border; 43 NC cells; and 27 interneurons). There was no significant difference between the number of cells recorded within the two age groups. T-test between 30+ mo EC-Tau vs 30+ mo Control group: Grid cell- P = 0.6658, t = 0.4450, df = 10; Head-direction cells- P = 0.2198, t = 1.309, df = 10; Border cells- P = 0.4608, t = 0.7670, df = 10; Interneurons- P = 1, t = 0.000, df = 10 and NC cells- P = 0.6658, t = 0.4450, df = 10. T-test between 14-mo EC-Tau vs 14-mo Control group: Grid cell- P = 0.8664, t = 0.712, df = 15; Head-direction cells- P = 0.4228, t = 0.8241, df = 15; Border cells- P = 0.6804, t = 0.4201, df = 15; Interneurons- P = 0.4366, t = 0.7993, df = 15 and Mann Whitney U test for NC cells- P = 0.3836, U = 26.50. Also, relative proportion of interneurons with respect to other excitatory cell types was not significantly different for 30+ mo group (EC-Tau: 19.84% and Control: 20.50%, T-test: P = 0.8772, t = 0.1586, df = 10) and 14-mo group (EC-Tau: 17.29% and Control: 22.73%, T-test: P = 0.4650, t = 0.498, df = 15). Note that the relative proportion of cells are the same across groups after increased recording sessions in the 30+ EC-Tau group. Head direction and border cells were identified as described previously (Bonnevie et al., 2013; Langston et al., 2010). Cells were classified as head-direction cells if they preferentially displayed tuning towards a particular direction in polar plots, while border cells were identified by their characteristic firing fields in the proximity of an environmental boundary. All other MEC cells sampled that did not exhibit spatial preference or directional tuning were identified as non-classified cells. Putative excitatory cells were distinguished from putative interneurons based on waveform width (> 350 μs) and firing rate (< 30 Hz).

Grid cell analysis and local field potentials

Firing rate distributions were determined by counting the number of spikes in each 2.5 cm × 2.5 cm bin as well as the time spent per bin. Maps for number of spikes and time were smoothed individually using a quasi-Gaussian kernel over the surrounding 5 × 5 bins. Analysis of grid cells was done as described previously (Bonnevie et al., 2013; Langston et al., 2010). Firing rates were determined by dividing spike number and time for each bin of the two smoothed maps. The peak rate was defined as the rate in the bin with the highest rate in the firing rate map. The structure of the rate maps was evaluated for all cells with more than 100 spikes by calculating the spatial autocorrelation for each smoothed rate map. The degree of spatial periodicity (grid scores) was determined for each recorded cell by taking a circular sample of the autocorrelogram, centered on the central peak but with the central peak excluded, and comparing rotated versions of this sample. Only cells that yielded positive grid scores were used for further analysis. To break the correlation between the firing pattern and animal's position, the data was shuffled 100 times by randomly shifting the spike times of each grid cell less than 20 seconds or more than 20 seconds relative to animal's position. Shuffling was done on the entire population of putative grid cells in the same age group and the same brain region (Figure S3A). Grid cells were defined as cells in which rotational symmetry based grid scores exceeded the 95^th percentile of a distribution of grid scores for shuffled data. Out of 170 putative grid cells only 122 cells exceeded the 95^th percentile criteria of 0.21. 76.9% of 14-mo control, 73.5% of 14-mo EC-Tau, 73% of 30+ mo control and 64% of 30+ EC-Tau cells passed the 0.21 threshold. Information content and spatial coherence was calculated as described previously (Hussaini et al., 2011). Spatial coherence estimates smoothness of a place field. It was calculated by correlating the firing rate in each pixel with firing rates averaged with its neighboring 8 pixels. It measures the extent to which the firing rate in a pixel is predicted by the rates in its neighbors (Muller and Kubie, 1989). Abrupt changes in firing rates of neighboring pixels make the place fields incoherent. Spatial information content is a measure used to predict the location of an animal from the firing of a cell. Information content was calculated using Skaggs' formula (Skaggs, 1993) and measures the amount of information carried by a single spike about the location of the animal and is expressed as bits per spike:

Spatial information content=∑Pi(RiR)log2(RiR)

Where: i is the bin/pixel number, Pi is the probability for occupancy of bin i, Ri is the mean firing rate for bin/pixel i and R is the overall firing mean rate. Head direction and border cells were identified as described previously (Bonnevie et al., 2013; Langston et al., 2010). Analysis of LFPs was done using EEGLAB (a toolbox for MATLAB) (Delorme and Makeig, 2004). Briefly, log mean spectra of LFPs were calculated and percent distribution of power was calculated for theta (4-12 Hz) and gamma (35-55 Hz) frequency range (Hussaini et al., 2011). LFP analysis was speed filtered between 5-25 cm/sec to exclude speed related theta or gamma increase. There was no significant difference between the overall running speeds of control mice vs the EC-Tau mice (data not shown). After the recording experiments were complete, all surviving animals were deeply anesthetized and perfused with 4% PFA solution. The brains were then extracted, placed in 4% PFA solution and shaken for 24 hrs at 4°C. The following day the brains were transferred to 30% sucrose solution and shaken at 4°C until they sunk to the bottom of the tube. Brains were frozen and sectioned (30 μm) using a cryostat and processed further for either immunohistochemistry (explained below) or to identify tetrode tracts where visible, into the MEC (Figures S3C-D).

Immunofluorescence

Free-floating brain sections were prepared in the same way as for immunohistochemistry. Sections (2 sections with 100 μm intervals from each mouse) were blocked in PBST containing 5% normal goat serum for 30 min at room temperature, and incubated with rabbit anti-TBR1 (Abcam, Cambridge, MA, USA; 1:500), rabbit anti-Parvalbumin (Thermo Scientific; 1:2000) or rat anti-Somatostatin (EMD Millipore; 1:100) antibody in PBST containing 5% normal goat serum at 4^oC overnight. After three washes with PBST, the sections were incubated with Alexa Fluor 594 goat anti-rabbit or Alexa Fluor 647 goat anti-rat IgG (Life Technologies, Grand Island, NY, USA; 1:1000) for 1 h at room temperature on a rotator. After three washes with PBST, the sections were blocked and incubated with antibody MC1 (1:1000) in PBST containing 5% normal goat serum at 4°C overnight. After three washes with PBST, the sections were incubated with Alexa Fluor 488 goat anti-mouse IgG (Life Technologies; 1:1000) for 1 h at room temperature. Following three washes with PBS, autofluorescence was quenched with 0.3% Sudan black in 70% ethanol (Decon Laboratories, King of Prussia, PA, USA) for 6 min at room temperature. The sections were rinsed with 70% ethanol and washed three times with 0.02% Tween-20 in PBS. The nuclei were stained with 5 μg/ml Hoechst33342 (Sigma-Aldrich) in PBST for 10 min at room temperature. Following three washes with PBS, sections were mounted on slides using SlowFade gold anti-fade reagent and imaged using confocal laser scanning microscopy via Z-stack. The number of TBR1⁺, PV⁺ or SOM⁺ neurons in the MEC was quantified automatically using ImageJ.

Resource for Cluster Analysis

BatchTINTV2 is a graphical user interface (GUI) created by our lab as an end-user friendly batch processing solution to complement Axona's new command line modification of TINT. BatchTINTV2 code is deposited to the GitHub repository (https://doi.org/10.5281/zenodo.200977).

supplement

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