Iron loading is a prominent feature of activated microglia in Alzheimer's disease patients

Abstract

Brain iron accumulation has been found to accelerate disease progression in amyloid-β(Aβ) positive Alzheimer patients, though the mechanism is still unknown. Microglia have been identified as key players in the disease pathogenesis, and are highly reactive cells responding to aberrations such as increased iron levels. Therefore, using histological methods, multispectral immunofluorescence and an automated in-house developed microglia segmentation and analysis pipeline, we studied the occurrence of iron-accumulating microglia and the effect on its activation state in human Alzheimer brains. We identified a subset of microglia with increased expression of the iron storage protein ferritin light chain (FTL), together with increased Iba1 expression, decreased TMEM119 and P2RY12 expression. This activated microglia subset represented iron-accumulating microglia and appeared morphologically dystrophic. Multispectral immunofluorescence allowed for spatial analysis of FTL⁺Iba1⁺-microglia, which were found to be the predominant Aβ-plaque infiltrating microglia. Finally, an increase of FTL⁺Iba1⁺-microglia was seen in patients with high Aβ load and Tau load. These findings suggest iron to be taken up by microglia and to influence the functional phenotype of these cells, especially in conjunction with Aβ.

Keywords: Alzheimer; Ferritin; Human; Iron; Microglia.

Fig. 1

Increased iron-positive and corresponding FTL⁺-microglia in Alzheimer’s disease a MTG cortex of Alzheimer patients shows increased positivity for iron inside cells with microglial morphology. b Significant increase of iron-positive cells in Alzheimer patients (n = 11) compared to controls (n = 9)(Mean, Student’s t-test). c Iron-positive microglia number only increased in cases with severe signal alterations on iron-sensitive T2*-w MRI, reflected by MRI severity score. d FTL expression reflects intracellular iron accumulation. Scale overview images, 200 μm. Scale zooms, 30 μm

Fig. 2

Schematic of mic-mIF acquisition and analysis pipeline

Fig. 3

Identification of homeostatic and activated Alzheimer-associated microglia clusters a Example of mIF image of an Alzheimer patient. b Exemplary images of segmented microglia in a control and an Alzheimer patient. c Number of identified cells in the GM and WM of controls (blue; n = 9) and Alzheimer patients (red; n = 12)(Mean, Student’s t-test). d Heatmap showing the expression of the four different markers (P2RY12, TMEM119, FTL and Iba1), in the 11 identified microglia clusters. e t-SNE plot of all individual cells showing the distinct colour-coded clusters and of control- vs. Alzheimer-patient-derived cells. f t-SNE plots colour-coded for intensity of the four individual markers. g Distribution of clusters in GM and WM. h Prevalence of identified clusters (C1–C11) in individual control (blue; n = 9) and Alzheimer patients (red; n = 12) (Median, Mann–Whitney U test). Scale bar, 100 µm. Scale bar zooms, 20 µm. GM Grey matter, WM White matter

Fig. 4

FTL⁺-microglia show significant Aβ-plaque infiltration a Schematic of how microglial Aβ-plaque infiltration is studied. Both cells and Aβ-plaques are identified and an interaction map showing ‘glyphs’ in the colour of the cluster of the infiltrating microglia is created. Subsequently glyphs are plotted back onto the whole slide image to also enable studying the spatial distribution pattern. Number of identified Aβ-plaques (Mean, Student’s t-test) (b) and the percentage of microglia infiltrated Aβ-plaques (Mean, Student’s t-test) (c) are increased in Alzheimer’s disease (n = 12) compared to controls (n = 9). d Microglia clusters differ spatially, depending on the presence of Aβ-plaques in their proximity. e Distribution of all-mic clusters compared to Aβ-mic clusters of controls and Alzheimer patients. Comparison of prevalence of all-mic compared to Aβ-mic of C1- (f) and C3-microglia (g) of all individual Alzheimer patients (n = 12) (paired Student’s t-test). h Percentage of all identified clusters infiltrating Aβ-plaques. i Representative images of C1 and C3-microglia infiltrating an Aβ-plaque. Scale bar, 50 µm. All mic = all microglia, Aβ-mic = Aβ-plaque infiltrating microglia

Fig. 5

C1-microglia (FTL⁺Iba1⁺) reflect iron-positive microglia a Number of identified C1-microglia correlates well with number of identified iron-positive microglia (n = 20, Pearson coefficient). Increased number of C1-microglia are associated with higher overall Aβ load (Thal) (b) and Tau Load (Braak) (c). Comparison between APOE4 (n = 6) vs. APOE3 (n = 4) carriers shows increased number of identified Aβ-plaques (d) and similar microglia infiltration (e). Increased prevalence of C1-all-mic (f), no increased proportion of Aβ-plaques infiltrated with C1-mic (Aβ-mic) (g), and significantly increased proportion of C1-microglia infiltrating Aβ-plaques (h). d and e Median, Mann–Whitney U test. Patients AD8 and AD12 were excluded from the APOE comparison analysis as they harbour a familial mutation in the APP and PSEN1 gene, respectively, which could be of more influence than the APOE-genotype

Fig. 6

C1 and C3-microglia show distinct dystrophic morphology compared to homeostatic control microglia a Representative images of the five different morphological subtypes of microglia: homeostatic, activated, dystrophic, phagocytic and macrophage-like. b Controls show predominantly homeostatic and activated microglia, while Alzheimer patients show a variety of homeostatic, activated, phagocytic and dystrophic microglia. c Representative images of C1 and C3-microglia surrounding Aβ-plaques showing dystrophic morphology. d 3D confocal imaging confirms cytorrhexic appearance of FTL⁺Iba1⁺-microglia. Scale bar represents 20 μm unless otherwise stated. Colorcoding for IF-images in 6A-C are according to the box in the top right corner. Colorcoding of 3D confocal images are according to the legend adjacent to the images