The role of microglia in AD pathophysiology is complex due to their diverse phenotypes and various activation pathways [29]. In the early stages of disease progression, microglia are considered beneficial as they proliferate, change morphology, and phagocytose harmful substances, including Aβ plaques and dead cells, after the surveillance and perception of neuronal damage in their microenvironment [16, 23]. Furthermore, microglia have been shown to provide protection and facilitate the repair of damaged neurons by secreting trophic factors [16]. However, activated microglia also trigger inflammatory pathways, resulting in the production of cytokines. This promotes further Aβ production, leading to excessive accumulation of harmful substances, reduces the release of trophic factors, and accelerates neuronal apoptosis and degenerative changes during the later stages of disease progression [30, 31]. Moreover, it has been shown that pTau could also negatively affect the phagocytic function of microglia by promoting their senescence, which leads to their dysfunctional phagocytic activity, exacerbates the accumulation of tau proteins and Aβ, and promotes neuronal damage and disease progression [32,33,34].

In this study, we explored the differences in the microglial populations between AD and healthy controls in the inferior and superior temporal regions of the inner retina. Our study showed a significantly reduced microglia population in AD retina compared to controls in the manual data (p = 0.0095). There was similar difference in the machine learning data but without reaching statistical significance (p = 0.0635). This difference may be due to the combination of the use of different fields of view (and thus different images) in the same set of specimens for the manual and machine learning data and the small sample size (n = 4 for control, and n = 5 for AD). Variability was also much lower in the manually obtained AD cell count. Due to this discrepancy between our manual and machine learning data, we cannot rule out the potential for a type I or type II error with our microglia count data. If microglia are indeed decreased in the AD retina, this finding might result from increased senescence and apoptosis of microglia secondary to the presence of tau proteins and neuroinflammation, and the reduction of microglia population could contribute to the decreased clearance of Aβ and NFTs in the neurons.

According to the literature, several studies have reported microgliosis, or an increase in the microglia population, in the AD retina, which contrasts with our findings [11, 35,36,37]. There may be several factors contributing to this discrepancy. First, most previous studies on the microglia population utilised cross-sections of the donor retina, which may yield different results compared to our study, which used punches of the full thickness retina tissue. Secondly, Koronyo et al. [37] suggested that their mapping of IBA-1 distribution was significantly greater in the C subregion, while most of our data was collected from the mid-peripheral region of the retina. Their normalised data, adjusted for retinal thickness, showed the most significant microgliosis in the mid-peripheral region in the superior temporal, whereas the majority of our samples were collected from the mid-peripheral region in the inferior temporal region. Thirdly, it should be noted that the donor demographic differs significantly between studies. In their study, 55% and 67% of the donors in the mild cognitive impairment (MCI) and AD groups, respectively, were Braak stage V–VI, while 100% of the AD donors in our study were Braak stage V–VI. One possible explanation is that the microglia population may change with the disease progression, potentially being higher in the earlier stage and decreasing as disease severity increases. In the initial stages of the disease, microglia may be recruited to clear Aβ, resulting in an increase in number and activation. However, as the Aβ load becomes too high for the resident microglia to effectively clear, it may lead to a loss in microglia count and function. In fact, Koronyo et al. [37] demonstrated that a lower proportion of microglia in the AD retina were engaging in Aβ uptake compared to normal cognition controls, implying impaired microglia function.

Although there is limited knowledge about changes in microglia population in AD, research findings from another neurodegenerative disease, dementia with Lewy Bodies (DLB), may provide some insights. DLB has shown increased microglial activation in the early stages of the disease, with significantly reduced transporter protein density indicative of microglia dystrophy in the late stage of the disease [38, 39].

Lastly it is important to consider that most other studies, including Koronyo et al. [37], investigated the microgliosis using the intensity of the immunoreactivity of the IBA-1 marker. This approach differs from counting microglia, as the fluorescent intensity of the marker is not only correlated with the microglia population but also affected by the size of the microglia. In our previous study [11] using retinal cross-sections, we indeed observed that there was increased IBA-1 immunoreactivity in the AD retina. However, this does not directly count the number of individual microglia. The current results show that AD microglia have larger area or volume than the control microglia, which would contribute to observation of increased amount of immunoreactivity.

Our findings revealed that the size of CD68 + /IBA-1 microglia was larger in the AD compared to the controls, whereas the size of the CD68-/IBA-1 microglia was not significantly different between AD and controls. Of note, majority of microglia population were CD68 + in both groups (88% in control and 92% in AD, no significant difference). CD68, a transmembrane glycoprotein embedded in lysosomal membrane, was used as a marker of phagocytic activity. We observed a higher volume of CD68 per microglia in AD, and it was the CD68 + microglia that were enlarged in AD, whereas the CD68- microglia were significantly smaller and similar between AD and control retina. The average measurements were greater in AD microglia than controls for all size parameters (cell area, cell volume, convex hull area, convex hull volume, cell perimeter, convex hull perimeter) in both manual and machine-learning data. The difference was statistically significant in all machine-learning parameters, but only in cell area for the manual parameters. This discrepancy in statistical significance is likely in part due to the difference in the number of measured microglia and the resulting statistical power between the two approaches: the manual measurements are from 3–4 sample microglia in 2D projection images whereas the machine learning measurements are from all microglia in entire z-stacks. There may have also been some sampling bias in the 2D method. The microglia were selected randomly using the grid method; however, the 2D projection step or the grid resolution could have resulted in a size-based bias. In Fig. 4, we note that although the mean values are similar between the manual and automated measurements, the distributions look quite different between the two methods. The machine learning values from all microglia are often long-tail and bottom-heavy distributed, but the manual measurement values from select microglia are more centrally distributed. The range of the values also differ; for example, for Cell Area the machine learning method reports microglia of above 600 µm2 and close to 900 µm2, but the largest microglia area reported by the manual method is less than 600 µm2. On the whole, the raw data points and their mean values in both manual and machine learning measurements demonstrate a consistent trend of larger microglia in AD than in control.

Increase in microglia size may reflect the accumulation of harmful pTau proteins and Aβ in the lysosomes, given the elevated levels of toxic Aβ and pTau present in the AD retina [40, 41], and the increased uptake of Aβ [37] and pTau [42, 43] by microglia in these conditions. Furthermore, it is established that the soma of microglia become enlarged when they are activated in an amoeboid morphology, as opposed to the ramified shape with a smaller soma. The proportion of CD68 + microglia is similar in both AD and control retinas at approximately 90%, and the number of CD68 + and CD68- phagocytic cups per microglia also does not differ between the two groups. However, increased CD68 immunoreactivity in individual microglia within the AD group suggests a higher level of activation in response to AD pathology. This highlights subtle but potentially important differences in microglial behavior between AD and control retinas. Our study provides a comprehensive, multi-faceted characterization of microglial activation. Another study showed that microgliosis occurs during AD, with increased Aβ deposits causing an increase in the number of microglia, while also a much fewer number of them are involved in Aβ uptake compared with normal controls [37]. Although many studies have examined the evidence of upregulated microglial responses in retina tissues from AD patients, our understanding of retinal microglial responses remains limited. Further research is needed to investigate the mechanisms and timeline of the changes in microglial population and size.

Microglia exhibit a variety of different morphologies that are associated with distinctive functions [44]. Their morphology has been shown to drastically change in different parts of the brain and with ageing [45]. Neurodegenerative diseases such as AD have also been associated with different microglia morphology [45]. The most abundant type of microglia morphology in a healthy adult CNS is the ramified phenotype, typically recognised with a small microglial soma connected to several long and ramified processes that are usually several times larger than the cell body itself [46]. The ramified microglia are responsible for surveillance during steady-state conditions [47], and they utilise their highly dynamic and mobile branches to survey their surroundings and detect any changes in the microenvironment. Once neuronal damage or toxic substances have been sensed, ramified microglia undergo an activation process and become ameboid-shaped with round and large cell bodies devoid of cell processes [44]. Unlike the ramified microglia for surveillance, the amoeboid morphology may allow for better mobility to the injured brain area and capacity for phagocytosis [47]. It has been shown that the morphological change between ramified to amoeboid microglia takes only 30–60 min in the brain [48, 49]. Other distinctive morphologies include hypertrophic/hyper-ramified, rod, dystrophic, and satellite microglia [44].

Previous studies have reported that there was an increased level of amoeboid microglia in the hippocampus and cerebral cortex in the AD brain [38, 50], and other studies have shown increased rod, hypertrophic/hyper-ramified, and dystrophic microglia during AD pathogenesis [44, 45]. However, there is a lack of reports of similar findings in the AD retina, which provides the rationale for this study, which focuses on the morphology of retinal IBA-1 labelled microglia within the AD eye of post-mortem donors using confocal microscopy. We investigated morphological features such as cell solidity, convexity, circularity, and phagocytic cup count between AD and control, and found no significant difference between the groups. It is worthwhile to mention that the microglia morphological changes are associated with specific retinal locations and the progression of neurodegenerative disease [45], and therefore, it is possible that the microglia morphology may be different between the inferior temporal of the mid-peripheral retina we used and other retinal regions. It is also important to consider that our study used AD donor tissues from Braak stage V–VI. Therefore, future studies investigating the microglia morphology in other regions of the retina and at different disease stages are warranted.