APOE4 expression confers a mild, persistent reduction in neurovascular function in the visual cortex and hippocampus of awake mice

Vascular factors are known to be early and important players in Alzheimer’s disease (AD) development, however the role of the ε4 allele of the Apolipoprotein (APOE) gene (a risk factor for developing AD) remains unclear. APOE4 genotype is associated with early and severe neocortical vascular deficits in anaesthetised mice, but in humans, vascular and cognitive dysfunction are focused on the hippocampal formation and appear later. How APOE4 might interact with the vasculature to confer AD risk during the preclinical phase represents a gap in existing knowledge. To avoid potential confounds of anaesthesia and to explore regions most relevant for human disease, we studied the visual cortex and hippocampus of awake APOE3 and APOE4-TR mice using 2-photon microscopy of neurons and blood vessels. We found mild vascular deficits: vascular density and functional hyperaemia were unaffected in APOE4 mice, and neuronal or vascular function did not decrease up to late middle-age. Instead, vascular responsiveness was lower, arteriole vasomotion was reduced and neuronal calcium signals during visual stimulation were increased. This suggests that, alone, APOE4 expression is not catastrophic but stably alters neurovascular physiology. We suggest this state makes APOE4 carriers more sensitive to subsequent insults such as injury or beta amyloid accumulation.


2-photon microscopy
In visual cortex, pial arterioles were identified by the presence of smooth muscle cells (in NG2-DSred animals), or by their morphology and orientation relative to the large pial veins, as well as their response to visual stimulation.Pial arterioles were then followed with x-y recordings (average pixel size: 0.42 x 0.42 μm 2 , imaging speed range 3.8-7.6Hz)made after bifurcations, until the vessels penetrated the parenchyma and therefore could no longer be classed as pial.Penetrating arterioles were followed and branching capillaries were imaged using high speed line scans, to record both diameter and RBCV (average pixel size: 0.19 μm, average number of traversals/second = 1224).In animals positive for GCaMP6f, additional x-y recordings (average pixel size: 1.28 x 1.28 μm 2 , imaging speed range 3.8-7.6Hz)were taken from the area proximate to vessel recordings at a depth that allowed for clear visualisation of neuronal cell bodies (layer 2/3 (~z = -150μm)).In CA1, images were taken up to ~500 μm from the surface of the window.Both x-y recordings of neuronal calcium across a large FOV (256 × 256 pixels, speed range 6.10-15.26Hz, speed average 7.75 Hz, pixel size average 1.80 μm) and smaller FOV recordings allowing for concurrent vascular and local neuronal calcium signals (256 × 256 pixels, speed range 3.05-7.63Hz, speed average 6.64 Hz; pixel size average 0.23 μm) were obtained.RBCV measurements were obtained as above using high speed linescans (average pixel size: 0.18 μm, average number of traversals/second = 791).

Data analysis
Preprocessing of images included registration, despeckling and/or median 3D filtering.In addition, all images had the 'stack contrast adjustment' plug-in applied to minimise any light artifacts arising from the visual stimulation.A custom MATLAB script was used to extract vessel diameter.
Briefly, a skeleton was generated along the centre of each vessel and an intensity profile was plotted along a line perpendicular to the vessel.Values were obtained across a window of five vessel skeleton pixels and averaged to obtain a mean value per window.This was repeated at every second skeleton point, allowing for diameter measurements to be made along the full vascular arbour.These measurements were then averaged along the vessel, resulting in an average diameter per frame.

RBCV measurements
In brief, the angle of shadows cast by the RBCs (that do not uptake fluorescent dextran) were measured and used to calculate the velocity across 40ms time windows that overlapped by 10 ms.
Angles that were extremely large or small (because of motion artefacts) were removed.To further account for noise in the data obtained from line scans, traces went through either one (diameter) or two (RBCV) iteration(s) of outlier removal.In addition, for stimulation experiments, trials missing greater than 10% of data were excluded and those that remained were subsequently smoothed using a loess smoothing method (range: 1-5% span of the total number of data points, depending on which best represented the shape of the data).To determine AUC measurements, data was interpolated to fill in missing values, using a moving mean average.

Calcium measurements
To find peaks in the calcium signal, traces were first scaled so that all values fell between zero and one (equation ii).Peaks during rest periods were identified as being at least twice the standard deviation of the whole trace, and 0.25 seconds apart from the next peak.Each original trace was then normalised as is standard, (equation iii) and the size of each peak was determined and the number of peaks per minute was computed.During rest, the correlation of ROIs within a field of view was measured.

Visual stimulation data analysis
Significant locomotion was defined as an event that was more than one third of a second in length and/or less than one second apart from adjacent locomotion epochs.
Data were normalised to the 5 s baseline preceding the onset of visual stimulation using equation (iii).To determine if there was a response to stimulation, a threshold was set as twice the standard deviation of this 5 s baseline period.Any response larger than this threshold was deemed 'responsive'.For neuronal data all trials were averaged per cell and if the mean response was larger than the threshold, it was deemed a responsive ROI and subsequently averaged to provide a mean response size per animal.
For vascular data, these trials were averaged per vessel to provide a 'responsive only' trace.Each vessel had a different number of contributing trials (because mice ran different amounts for each recording) and those with a low number of trials were likely to provide more extreme response frequency values (e.g., 0% or 100%).Therefore, the number of contributing trials per vessel was used to weight data when calculating response frequency.When calculating the size of responses, area under the curve (AUC) measurements were taken during the stimulation period using the inbuilt MATLAB function "trapz" which utilizes trapezoidal numerical integration.

Vascular density calculations
Animals were first perfused with 0.1 M phosphate buffered saline (PBS) then 4% paraformaldehyde (PFA) in PBS, and were then perfused with 5% gelatin containing 0.2% FITCconjugated albumin (at 37 ℃).Following at least 30 minutes on ice, brain tissue was then extracted and stored in 4% PFA at 4˚C for 24 hours before being transferred to 30% sucrose in PBS for at least 3 days.Tissue was then sliced at 200µm on a vibratome, and slices were imaged using confocal microscopy (Leica SP8, pixel size: 0.45 -0.57m).

Sample Size Considerations
To estimate the power of our findings, we have calculated the power post hoc.For our main findings of stimulus-induced calcium change (Figure 2) and vessel responsiveness (Figure 3) we have calculated the effect size (by taking the difference between group means and dividing by the standard deviation of the E3 group, as per Cohen ( 1988)), and used both effect and sample sizes to calculate power post-hoc.Our effect size for stimulus-induced calcium responses was 2.47 (nE3=6, nE4=6), resulting in >96% power for detecting differences between APOE groups at alpha = 0.05.Our effect size for vessel responsiveness in arterioles was 0.33 (nE3=33, nE4=37) and capillaries 0.32 (nE3=60, nE4=64), meaning our power to detect differences between APOE groups at alpha = 0.05 was low at 28% for arterioles and 40% for capillaries.However, the design of the whole study includes multiple replications of the key findings at different age points, and with consistent results being found across brain regions, mitigating this low power.In fact, huge sample sizes would be needed (of >155 capillaries & >160 arterioles per APOE group) to get to a power of 80%, and this would be unfeasible (>630 total vessels) and require 5x as many animals to undergo invasive surgery.Our animal numbers (6 animals per APOE group) are within the range recommended using the resource equation approach, Arifin & Zahiruddin, 2017; sample size = DF/group size + 1, where the aim is to have between 10 and 20 degrees of freedom).