Fibrillar Aβ triggers microglial proteome alterations and dysfunction in Alzheimer mouse models

Amyloid plaque deposits appear at similar ages (between 6–8 weeks) in APPPS1 and APP-KI mouse models (Radde et al., 2006; Saito et al., 2014). To reveal the dynamics of microglial proteomic alterations across different amyloid stages, we analyzed microglia from 1, 3, 6 and 12 month old APPPS1 and APP-KI mice and their corresponding age-matched wild-type (WT) mice (Figure 1—figure supplement 1A). To facilitate proteomic analysis, we first optimized the microglial isolation procedure. CD11b positive microglia were isolated from mouse cerebrum using MACS technology. The purity of the CD11b-enriched fraction was controlled by fluorescence activated cell sorting (FACS), revealing that 97% of isolated cells were CD11b positive (Figure 1—source data 1A). Of note, only 0.49% of CD11b positive cells were detected in the CD11b-depleted fraction (Figure 1—source data 1B), demonstrating high isolation efficiency. Isolated microglia were lysed and then measured by LC-MS/MS using label-free quantification (LFQ) of proteins. Next, we optimized the data acquisition method for microglial proteome analysis. Recently, it was shown that Data Independent Acquisition (DIA) for LFQ of proteins identifies and quantifies consistently more peptides and proteins across multiple samples, compared to Data Dependent Acquisition (DDA) (Bruderer et al., 2015). Thus, we first evaluated the performance of DDA vs. DIA using microglial lysates from WT and APPPS1 mice. DDA identified 53912 peptides on average compared to 74281 peptides identified by DIA, representing a 37.8% increase in detection by DIA method (Supplementary file 1). Overall, the main advantage of DIA was the improved consistency of protein quantifications among the replicates and the identification of proteins with lower abundance, leading to a 29% increase of relatively quantified proteins from 4412 with DDA to 5699 with DIA (Figure 1—figure supplement 1B and C, Supplementary file 1). Therefore, we chose the DIA acquisition method to also generate the proteome dataset of APP-KI microglia. We detected a consistent relative quantification of proteins with an overlap of 93.5% (5500 proteins) between the two investigated mouse models (Figure 1—figure supplement 1D), supporting our selection of DIA as a robust method for microglial proteomic analysis. For the comparative analysis of proteomic changes, we defined a threshold of a log2 fold change larger than 0.5 or smaller than −0.5 with a p-value less than 0.05, and significance after False Discovery Rate (FDR) correction. No data imputation was performed.

According to Aβ burden in both mouse models, we refer to one month of age as a pre-deposition stage, and to 3, 6 and 12 months of age as early, middle and advanced stages of amyloid pathology, respectively (Figure 1—figure supplement 2). At the pre-deposition stage (1 month), microglial proteomes of APPPS1 and APP-KI mice did not show significant alterations compared to WT (Figure 1A and B), demonstrating that microglia are not affected prior to development of Aβ pathology. At 3 months of age, microglia in APPPS1 mice already displayed a significant up-regulation of 332 proteins and down-regulation of 678 proteins, compared to WT microglia (Figure 1C, Supplementary file 2A). In contrast, APP-KI microglia were hardly affected at 3 months of age (Figure 1D, Supplementary file 2B), which is particularly surprising because both mouse models show comparable amyloid burden at this stage (Figure 1—figure supplement 2). At 6 months of age, microglia in APPPS1 mice displayed 309 up-regulated and 261 down-regulated proteins, compared to WT microglia (Figure 1E, Supplementary file 2A). In contrast to 3 months of age (Figure 1D), APP-KI mice displayed a substantial alteration of their microglial proteome at 6 months of age, illustrated by 140 up-regulated and 151 down-regulated proteins (Figure 1F, Supplementary file 2B). Still, microglial alterations in 6 month old APP-KI mice were less pronounced compared to the proteome of APPPS1 mice (Figure 1E and F). Noteworthy, by 12 months of age, APPPS1 microglia revealed a significant up-regulation of 776 proteins and down-regulation of 633 proteins, while APP-KI microglia displayed 704 up-regulated and 666 down-regulated proteins (Figure 1G and H, Supplementary file 2A and B). Overall, our data show that amyloid plaque accumulation triggers microglial progression towards an AD-associated phenotype in both mouse models, but that response dynamics are different in APPPS1 and APP-KI microglia.

Next, we determined protein alterations that first appear in early, middle or advanced stages of Aβ deposition and remain altered through all analyzed stages, thus following amyloid accumulation. To this end, we selected the APPPS1 mouse model as a reference since it displays earlier changes and therefore provides a better time resolution of protein alterations to amyloid response, compared to the APP-KI model (Figure 2A). Only proteins with a consistent quantification in all samples of an age group were used for relative quantification. Furthermore, in order to determine robust and model-independent Aβ-triggered microglial alterations, we only selected MARPs that were altered with a significantly changed abundance in both mouse models (even if in APP-KI microglia changes appear later). This analysis identified 90 early, 176 middle, and 435 advanced MARPs (Figure 2—source data 1, Figure 2—figure supplement 1A). The most strongly regulated MARPs with early, middle and advanced response are displayed in corresponding heatmaps (Figure 2B–D).

Early MARPs included several of the previously identified transcriptional DAM markers (Keren-Shaul et al., 2017) such as ITGAX (CD11c), APOE, CLEC7a, LGALS3 (Galectin-3) and CD68, which were found with an increased abundance (Figure 2B). Moreover, proteins involved in antigen presentation such as CD74, H2-D1, TAP2, TAPBP and H2-K1 were revealed as up-regulated early MARPs. In addition, we discovered prominent changes in interferon signaling represented by the up-regulation of early MARPs, including MNDA, OAS1A, IFIT3, ISG15, GVIN1, STAT1 and 2 (Figure 2B). Even though early MARPs were mainly up-regulated, we also identified early MARPs with a decreased abundance, including KRAS, a protein involved in cell proliferation and the endocytosis regulator EHD2 among others (Figure 2B). A gene ontology (GO) cluster enrichment analysis of early MARPs revealed that up-regulated proteins were enriched for immune and viral response, interferon beta and cytokine response, antigen processing and presentation as well as biotic and lipid response (Figure 3A, Figure 3—figure supplement 1A and D). Thus, these processes represent first molecular alterations which progressively follow Aβ plaque pathology.

The middle MARPs included the up-regulated proteins FABP3, FABP5, CD63, TREM2, MIF and GUSB (Figure 2C), demonstrating a progressive conversion of the microglial proteome towards a disease state that accompanies Aβ accumulation. Importantly, middle MARPs also reveal down-regulation of the proposed homeostatic markers such as CX3CR1, TMEM119 and P2RY12 (Figure 2C). Among the down-regulated middle MARPs, we identified additional chemotaxis and cell migration related proteins like SYK, FER, CX3CL1, and BIN2 (Figure 2C, Figure 2—source data 1), underscoring a loss of key microglial functions throughout AD progression.

Advanced MARPs represent proteins that were only altered upon extensive amyloid pathology. This group included up-regulation of proteins involved in calcium ion binding such as NCAN, MYO5A, HPCAL4, TTYH1 and GCA and down-regulation of proteins that play a role in the endocytosis/lysosomal system such as TFEB, TFE3 and BIN1 (Figure 2D, Figure 2—source data 1). In addition, different G protein-coupled receptor signaling proteins, including GNG2, GNG5 and GNG10, also displayed a decreased abundance (Figure 2D). Furthermore, we observed a high correlation of MARP signatures between the two models at 12 months (Figure 2—figure supplement 1B).

A GO cluster enrichment analysis of middle and advanced MARPs identified down-regulation of biological processes including cell motility, migration and chemotaxis, as well as cell development and proliferation (Figure 3B and C, Figure 3—figure supplement 1B,C,E and F). Conversely, we found an up-regulation of protein glycosylation and carbohydrate metabolism (Figure 3C, Figure 3—figure supplement 1E and F). Additionally, alterations in ion transport processes involving ion homeostasis and pH regulation were also detected (Figure 3C). These findings indicate that after an initial inflammatory response, several cellular processes related to chemotaxis and phagocytosis are progressively dysregulated upon increased Aβ deposition.

Importantly, our proteomic analysis also detected alterations in proteins related to different genetic risk factors of AD (Karch and Goate, 2015), including significantly increased levels of APOE, TREM2, and INPP5D, and decreased levels of PLCG2, ABI3, and BIN1 in both mouse models (Figure 2—figure supplement 2A and B).

In addition, we compared MARP signatures with the previously published single cell transcriptome study of 5xFAD mice (Keren-Shaul et al., 2017) to visualize the overlap, as well as differences, between proteomic and transcriptomic microglial profiles (Figure 2B–D). When comparing the overlapping proteome of 12 months old APPPS1 and APP-KI mice with the transcriptome of 5xFAD mice (Keren-Shaul et al., 2017), we were able to quantify 3348 common proteins/transcripts, whereas 2152 and 2841 gene products were only quantified on protein and transcript level, respectively (Figure 4A). Comparison of our proteomic signatures with the microglial transcripts (Keren-Shaul et al., 2017) revealed an overlap of 227 unidirectionally regulated, whereas 263 and 849 gene products were only regulated on protein and transcript level, respectively (Figure 4B). While transcriptomics demonstrates a similar regulation of a large number of early MARPs, we found less overlap for middle and advanced MARPs (Figure 2B–D). We also identified proteins with an inverse regulation compared to transcriptomic signatures such as the early MARP RPL38, middle MARPs MCM3 and GFPT1 or advanced MARPs CDC88A, GALNT2, EIF4B and CHMP6 (Figure 2B–D, Figure 2—source data 1). Furthermore, the advanced MARP HEXB showed a consistent up-regulation in our proteomic analysis (Figure 2—source data 1), despite being previously anticipated as a homeostatic gene.

Overall, our study presents a robust and reliable method to track microglial proteome and provides a resource that maps changes in brain immunity during different phases of Aβ accumulation.

Next, we validated proteomic changes by western blot analysis using isolated microglia from 12 month old APPPS1 and APP-KI mice. This analysis confirmed the pronounced increase of the early MARPs APOE and CD68, the middle MARPs TREM2 and FABP5, as well as reduced levels of the advanced MARP CSF1R (Figure 4C) in both transgenic mouse models compared to WT mice. Furthermore, proteomic changes were also validated by immunohistochemistry in order to visualize spatial distribution of altered microglial proteins in APPPS1 and APP-KI mice. Immunohistological analysis of 3 month old APPPS1 mice already revealed increased immunoreactivity of selected MARPs such as CLEC7a (Figure 5), TREM2 (Figure 5—figure supplement 1) and APOE (Figure 5—figure supplement 2) that mark initial stages of microglial activation in AD. This increase was detected in IBA1 positive microglia that were clustering around amyloid plaques, but not in microglia further away from plaques and was – in agreement with our proteomic data – less pronounced in 3 month old APP-KI mice. Accordingly, at 12 months, both APPPS1 and APP-KI mice showed a similar increase in the levels of selected MARP CLEC7a (Figure 6) and decreased levels of TMEM119 (Figure 7) compared to the WT mice, once again in microglia surrounding amyloid plaques. Taken together, we validated selected microglial proteomic alterations from our dataset by applying biochemical and immunohistochemical methods. In addition, we confirmed the kinetic differences in AD-associated proteomic signatures of APPPS1 and APP-KI microglia. Our data suggest that interaction between microglia and Aβ is likely triggering the proteomic changes as they could be observed in plaque-associated microglial population.

The magnitude of proteomic microglial changes was found to correlate with Aβ plaque accumulation throughout disease progression. However, the appearance of MARP signatures differed between the models and occurred earlier in the APPPS1 mice (Figure 1C and D, Figure 2A) despite the comparable plaque load observed in both mouse models (Figure 1—figure supplement 2). Thus, it appears possible that the nature of amyloid plaques is different between the APPPS1 and APP-KI mice. To examine this, we analyzed amyloid plaques in 3, 6 and 12 month old APPPS1 and APP-KI mice by immunohistochemistry. We used the anti-Aβ antibody NAB228 (Abner et al., 2018) to detect Aβ plaques, and Thiazine Red (ThR) to visualize fibrillar amyloid (Daria et al., 2017; Figure 8A). In agreement with amyloid plaque pathology reported in this model (Radde et al., 2006), APPPS1 mice contained fibrillar amyloid plaque cores already at 3 months of age. In contrast, fibrillar Aβ was barely detectable in APP-KI mice at 3 months of age (Figure 8A). The amount of fibrillar Aβ in APP-KI mice increased at 6 and 12 months, but overall remained lower compared to the APPPS1 mice (Figure 8A). The reduced levels of fibrillar Aβ in APP-KI mice were also confirmed by biochemical analysis in which fibrillar Aβ was specifically detected via immunoblot of the insoluble brain fraction (Figure 8B). Despite the reduced levels of fibrillar Aβ, Aβ coverage was increased in 3 month old APP-KI compared to the APPPS1 mice (Figure 8C). To obtain further information on the conformational state of amyloid plaque cores in both mouse models, we performed spectral analysis using two luminescent conjugated oligothiophenes (LCOs) as reported previously (Rasmussen et al., 2017; Figure 8—figure supplement 1A–D). The quadro-formyl thiophene acetic acid (qFTAA) LCO binds to dense core amyloid fibrils and the hepta-formyl thiophene acetic acid (hFTAA) LCO seems to recognize both amyloid fibrils and less dense pre-fibrillar amyloid aggregates (Klingstedt et al., 2011; Nyström et al., 2013). In contrast to APP-KI, a prominent qFTAA signal was detected in APPPS1 mice at 3 months of age, revealing dense core fibrillar Aβ (Figure 8—figure supplement 1A and C). As expected, we could detect the hFTAA signal in both models at 3 months of age, visualizing the less dense, pre-fibrillar amyloid aggregates. To compare the levels of dense fibrillar versus pre-fibrillar Aβ, we quantified the ratio between peak intensities of qFTAA (emission at 502) and hFTAA (emission at 588) in both models at 3 and 12 months (Figure 8—figure supplement 1D). This analysis revealed significantly reduced levels of fibrillar Aβ in the APP-KI mice at the age of 3 months and only a trend towards reduction at the age of 12 months (Figure 8—figure supplement 1A–D). Thus, we demonstrate prominent differences in Aβ plaque fibrillization between APPPS1 and APP-KI mice at the age of 3 months.

To determine what triggers microglial reactivity in AD, we first quantified microglial recruitment to Aβ plaques in both mouse models. This analysis was done at the early pathological stage (3 months), where we identified prominent differences in the proteome regulation (Figure 1C and D, Figure 2A) as well as in the amount of fibrillar Aβ (Figure 8A and B, Figure 8—figure supplement 1A–C) between the two mouse models. Immunohistochemical analysis revealed IBA1 positive, amoeboid microglia recruited to large, ThR positive, fibrillar Aβ aggregates in APPPS1 mice (Figure 9A). Of note, we observed intracellular fibrillar Aβ in APPPS1 microglia in close contact to the plaque core as previously reported (Bolmont et al., 2008). Despite the significantly smaller fibrillar Aβ aggregates in APP-KI mice, we could observe IBA1 positive microglia polarized towards the fibrillar Aβ, rather than to the surrounding non-fibrillar Aβ positive material (Figure 9A). Quantification analysis revealed increased clustering of IBA1 positive microglia around Aβ plaques in APPPS1 compared to the APP-KI mice (Figure 9B), despite their overall larger Aβ plaque size (Figure 9C), supporting that fibrillar Aβ conformation, rather than plaque size, are responsible for microglial recruitment. Likewise, we observed increased CD68 immunoreactivity around Aβ plaques in the APPPS1 compared to the APP-KI mice (Figure 9D and E). However, CD68 signal per individual microglial cell in the plaque vicinity was similar in both models (Figure 9F), suggesting that differences in AD-associated microglial proteins are due to the number of recruited microglia rather than differences in their individual CD68 protein levels.

One of the Aβ modifications that can be readily detected in AD brains and favours fibrillar conformation is pyroglutamate-modified Aβ (pE3-Aβ) (Dammers et al., 2015a; Dammers et al., 2015b). To test whether pE3-Aβ may be triggering microglial reactivity, we assessed pE3-Aβ levels in 3 and 12 month old APPPS1 and APP-KI mice by immunohistochemistry, using a previously validated antibody (Hartlage-Rübsamen et al., 2018; Figure 9—figure supplement 1A–D). As we could only detect pE3-Aβ reactivity in 12 month old animals, but not at 3 months (Figure 9—figure supplement 1A and B), it is less likely that this modification is responsible for microglial recruitment and differences in the proteome between the models. Similarly as observed for fibrillar Aβ, levels of pE3-Aβ were reduced in the APP-KI compared to the APPPS1 mice (Figure 9—figure supplement 1C and D).

Besides Aβ, microglial recruitment has also been associated with neuritic damage (dystrophic neurites) (Hemonnot et al., 2019). Accordingly, we analyzed dystrophic neurite pathology in 3 month old APPPS1 and APP-KI mice, using an antibody against APP that accumulates in these structures (Cummings et al., 1992; Sadleir et al., 2016). As previously reported (Radde et al., 2006), amyloid plaques in the APPPS1 mice were surrounded by prominent dystrophic neurites (Figure 9G). Interestingly, despite the reduced load of fibrillar Aβ, we readily detected dystrophic neurites in the APP-KI mice (Figure 9G). Moreover, our quantification analysis revealed an increased dystrophic neurite area in the APP-KI compared to the APPPS1 mice (Figure 9H) and reduced number of microglial cells recruited to this area (Figure 9I). Therefore, the differences in early microglial recruitment to APPPS1 plaques and the consecutive proteomic changes are less likely to be triggered by dystrophic neurites.

Altogether, we hypothesize that microglial recruitment may be triggered by the fibrillar Aβ content of amyloid plaques, which drives the acquisition of MARP signatures.

The differences observed in the dynamics of microglial response to amyloid in the APPPS1 and the APP-KI mice prompted us to examine the association between microglial phagocytic function and the appearance of MARP signatures. To this end, we assessed the phagocytic capacity of microglia from 3 and 6 month old APPPS1 and APP-KI mice compared to the corresponding age-matched WT microglia using the E. coli-pHrodo uptake assay (Götzl et al., 2019; Kleinberger et al., 2014). We already detected phagocytic impairments in 3 month old APPPS1 microglia, which was reflected by a prominent decrease in the amount of intracellular E. coli particles (Figure 9J, Figure 9—source data 1A) and a reduced number of CD11b positive cells that were capable of E. coli uptake (Figure 9K, Figure 9—source data 1B). Notably, APPPS1 phagocytic impairment did not change further in 6 month old microglia, suggesting that microglial functional deficits, as measured by the E. coli uptake assay, were fully established already at 3 months of age and characterized by early MARPs. In contrast, APP-KI microglia remained functional at 3 months, but at 6 months displayed similar impairments as seen in APPPS1 microglia (Figure 9J and K). Overall, we observed different kinetics of microglial dysfunction among mouse models which correlate with the appearance of MARPs and, in turn, with the presence of fibrillar Aβ.

Male and female mice of the hemizygous APPPS1 mouse line overexpressing human APP_KM670/671NL and PS1_L166P under the control of the Thy1 promoter (Radde et al., 2006), homozygous App^NL-G-F mouse line (Saito et al., 2014) and the C57BL/6J (WT) line were used in this study. Mice were group housed under specific pathogen-free conditions. Mice had access to water and standard mouse chow (Ssniff Ms-H, Ssniff Spezialdiäten GmbH, Soest, Germany) ad libitum and were kept under a 12/12 hr light-dark cycle in IVC System Typ II L-cages (528 cm²) equipped with solid floors and a layer of bedding. All animal experiments were performed in compliance with the German animal welfare law and have been approved by the government of Upper Bavaria.

Primary microglia were isolated from mouse brains (cerebrum) using MACS technology (Miltenyi Biotec) according to manufacturer's instructions and as previously described (Daria et al., 2017). Briefly, olfactory bulb, brain stem and cerebellum were removed and the remaining cerebrum was freed from meninges and dissociated by enzymatic digestion using a Neural Tissue Dissociation Kit P (Miltenyi Biotec). Subsequently, mechanical dissociation was performed by using three fire-polished glass Pasteur pipettes of decreasing diameter. CD11b positive microglia were magnetically labelled using CD11b MicroBeads, loaded onto a MACS LS Column (Miltenyi Biotec) and subjected to magnetic separation, resulting in CD11b-enriched (microglia-enriched) and CD11b-depleted (microglia-depleted) fractions. Obtained microglia-enriched pellets were either washed twice with HBSS (Gibco) supplemented with 7 mM HEPES, frozen in liquid nitrogen and stored at −80°C for biochemical or mass spectrometry analysis, or resuspended in microglial culturing media and used for phagocytosis assay as described below.

Microglia were isolated from three transgenic animals and their three corresponding WT controls for each age and genotype. We included both male and female mice into proteomic analysis and their distribution is outlined in Supplementary file 3. Microglia-enriched pellets from individual animals were analysed separately and lysed in 200 µL of STET lysis buffer (50 mM Tris, 150 mM NaCl, 2 mM EDTA, 1% Triton, pH 7.5) at 4°C with intermediate vortexing. The samples were centrifuged for 5 min at 16000 x g at 4°C to remove cell debris and undissolved material. The supernatant was transferred to a LoBind tube (Eppendorf) and the protein concentration estimated using the Pierce 660 nm protein assay (ThermoFisher Scientific). A protein amount of 15 µg was subjected to tryptic protein digestion applying the the filter aided sample preparation protocol (FASP) (Wiśniewski et al., 2009) using Vivacon spin filters with a 30 kDa cut-off (Sartorius). Briefly, proteins were reduced with 20 mM dithiothreitol and free cystein residues were alkylated with 50 mM iodoacetamide (Sigma Aldrich). After the urea washing steps, proteins were digested with 0.3 µg LysC (Promega) for 16 hr at 37°C followed by a second digestion step with 0.15 µg trypsin (Promega) for 4 hr at 37°C. The peptides were eluted into collection tubes and acidified with formic acid (Sigma Aldrich). Afterwards, proteolytic peptides were desalted by stop and go extraction (STAGE) with self-packed C18 tips (Empore C18 SPE, 3M) (Rappsilber et al., 2003). After vacuum centrifugation, peptides were dissolved in 20 µL 0.1% formic acid (Biosolve) and indexed retention time peptides were added (iRT Kit, Biognosys).

For label-free quantification (LFQ) of proteins, peptides were analyzed on an Easy nLC 1000 or 1200 nanoHPLC (Thermo Scientific) which was coupled online via a Nanospray Flex Ion Source (Thermo Sientific) equipped with a PRSO-V1 column oven (Sonation) to a Q-Exactive HF mass spectrometer (Thermo Scientific). An amount of 1.3 µg of peptides was separated on in-house packed C18 columns (30 cm x 75 µm ID, ReproSil-Pur 120 C18-AQ, 1.9 µm, Dr. Maisch GmbH) using a binary gradient of water (A) and acetonitrile (B) supplemented with 0.1% formic acid (0 min., 2% B; 3:30 min., 5% B; 137:30 min., 25% B; 168:30 min., 35% B; 182:30 min., 60% B) at 50°C column temperature.

For DDA, full MS scans were acquired at a resolution of 120000 (m/z range: 300–1400; automatic gain control (AGC) target: 3E+6). The 15 most intense peptide ions per full MS scan were selected for peptide fragmentation (resolution: 15000; isolation width: 1.6 m/z; AGC target: 1E+5; normalized collision energy (NCE): 26%). A dynamic exclusion of 120 s was used for peptide fragmentation.

For DIA, one scan cycle included a full MS scan (m/z range: 300–1400; resolution: 120000; AGC target: 5E+6 ions) and 25 MS/MS scans covering a range of 300–1400 m/z with consecutive m/z windows (resolution: 30000; AGC target: 3E+six ions;). The maximum ion trapping time was set to ‘auto’. A stepped normalized collision energy of 26 ± 2.6% was used for fragmentation. Supplementary file 4

Microglia from APPPS1 mice were analyzed using DDA and DIA for method establishement. Microglia from APPPS1 and APP-KI mice were compared using DIA as it outperformed DDA.

For data acquired with DDA, the data was analyzed with the software Maxquant (maxquant.org↗, Max-Planck Institute Munich) version 1.6.1.0 (Cox et al., 2014). The MS data was searched against a reviewed canonical fasta database of Mus musculus from UniProt (download: November the 1^st 2017, 16843 entries) supplemented with the sequence of human APP with the Swedish mutant and the iRT peptides. Trypsin was defined as a protease. Two missed cleavages were allowed for the database search. The option first search was used to recalibrate the peptide masses within a window of 20 ppm. For the main search peptide and peptide fragment mass tolerances were set to 4.5 and 20 ppm, respectively. Carbamidomethylation of cysteine was defined as static modification. Acetylation of the protein N-term as well as oxidation of methionine was set as variable modification. The FDR for both peptides and proteins was set to 1%. The ‘match between runs’ option was enabled with a matching window of 1.5 min. LFQ of proteins required at least one ratio count of unique peptides. Only unique peptides were used for quantification. Normalization of LFQ intensities was performed separately for the age groups because LC-MS/MS data was acquired in different batches.

A spectral library was generated in Spectronaut (version 12.0.20491.11, Biognosys) (Bruderer et al., 2015) using the search results of Maxquant of the APPPS1 dataset. The library includes 122542 precursor ions from 91349 peptides, which represent 6223 protein groups. The DIA datasets of both mouse models were analyzed with this spectral library (version 12.0.20491.14.21367) with standard settings. Briefly, the FDR of protein and peptide identifications was set to 1%. LFQ of proteins was performed on peptide fragment ions and required at least one quantified peptide per protein. Protein quantification was performed on maximum three peptides per protein group. The data of APPPS1 microglia was organized in age-dependent fractions to enable separate normalization of the data. All LC-MS/MS runs of the APP-KI dataset were normalized against each other because all samples were analyzed in randomized order in one batch.

The protein LFQ reports of Maxquant and Spectronaut were further processed in Perseus (Tyanova et al., 2016). The protein LFQ intensities were log2 transformed and log2 fold changes were calculated between transgenic and WT samples separately for the different age groups and mouse models. Only proteins with a consistent quantification in all samples of an age group were considered for statistical testing. A two-sided Student’s t-test was applied to evaluate the significance of proteins with changed abundance (from log2 fold change of transgenic versus WT microglia per age group). Additionally, a permutation based FDR estimation (threshold: FDR = 5%, s₀ = 0.1) was used to perform multiple hypothesis correction (Tusher et al., 2001). A log2 fold change larger than 0.5, or smaller than −0.5, a p-value less than 0.05 and significant regulation after FDR filtering were defined as regulation thresholds criteria. The same thresholds were used for the comparison with transcriptomics data.

Gene ontology enrichment analysis was performed with the web-tool DAVID (version 6.8) (Huang et al., 2009a; Huang et al., 2009b) using GO_FAT terms. Up- and down-regulated early, middle and advanced MARPs were clustered separately for biological process, cellular component, and molecular function with all 5500 proteins, consistently quantified in APPPS1 and APP-KI microglia, as a customized background. A medium classification stringency was applied. An enrichment score of 1.3 was defined as threshold for cluster enrichment.

RIPA lysates were prepared from brain hemispheres, centrifuged at 100000 x g (60 min at 4°C) and the remaining pellet was homogenized in 0.5 mL 70% formic acid. The formic acid fraction was neutralized with 20 × 1 M Tris-HCl buffer at pH 9.5 and used for Aβ analysis. For Aβ detection, proteins were separated on Tris-Tricine gels (10–20%, Thermo Fisher Scientific), transferred to nitrocellulose membranes (0.1 µm, GE Healthcare) which were boiled for 5 min in PBS and subsequently incubated with the blocking solution containing 0.2% I-Block (Thermo Fisher Scientific) and 0.1% Tween 20 (Merck) in PBS for 1 hr, followed by overnight incubation with rabbit polyclonal 3552 antibody (1:2000, Yamasaki et al., 2006). Antibody detection was performed using the corresponding anti-HRP conjugated secondary antibody (Santa Cruz) and chemiluminescence detection reagent ECL (Thermo Fisher Scientific).

Microglia-enriched pellets were resuspended in 100 µL of STET lysis buffer (composition as described above for mass spectrometry, supplemented with protease and phosphatase inhibitors), kept on ice for 20 min and then sonicated for 4 cycles of 30 s. Cell lysates were then centrifuged at 9600 x g (5 min. at 4°C) and pellets discarded. Protein concentration was quantified using Bradford assay (Biorad) according to manufacturer instructions. 10 µg of two independent microglial lysates per genotype were loaded on a bis-tris acrylamide gel (8% or 12%) and subsequently blotted onto either a PVDF or nitrocellulose membrane (Millipore) using the following antibodies: TREM2 (1:10, clone 5F4, Xiang et al., 2016); APOE (1:1000, AB947 Millipore); CD68 (1:1000, MCA1957GA, AbDserotec); CSF1R (1:1000, 3152, Cell Signaling) and FABP5 (1:400, AF1476, R and DSystems). Blots were developed using horseradish peroxidase-conjugated secondary antibodies (Promega) and the ECL chemiluminescence system (Amersham) or SuperSignal West Pico PLUS (Thermo Scientific). An antibody against GAPDH (1:2000, ab8245, Abcam) was used as loading control.

We analyzed 3 and 12 month old APPPS1 and APP-KI mice of both sexes, as outlined in Supplementary file 5. Mice were anesthetized i.p. with a mixture of ketamine (400 mg/kg) and xylazine (27 mg/kg) and transcardially perfused with cold 0.1M PBS for 5 min followed by 4% paraformaldehyde (PFA) in 0.1 M PBS for 15 min. Brains were isolated and postfixed for 20 min in 4% PFA in 0.1 M PBS and transferred to 30% sucrose in 0.1 M PBS for cryopreservation. Brains were embedded in optimal cutting temperature compound (Tissue-Tek O.C.T., Sakura), frozen on dry ice and kept at −80°C until sectioning. 30 µm coronal brain sections were cut using a cryostat (CryoSTAR NX70, Thermo Scientific) and placed in 0.1 M PBS until staining. Alternatively, sections were kept in anti-freezing solution (30% glycerol, 30% ethylenglycol, 10% 0.25 M PO₄ buffer, pH 7.2–7.4% and 30% dH₂O) at −20°C and briefly washed in 0.1M PBS before staining. Briefly, free-floating sections were permeabilized with 0.5% Triton-PBS (PBS-T) for 30 min, blocked either in 5% normal goat Serum or 5% donkey serum in PBS-T for 1 hr and incubated overnight at 4°C in blocking solution with the following primary antibodies: IBA1 (1:500, 019–19741,Wako), IBA1 (1:500, ab5076, Abcam), NAB228 (1:2000, sc-32277, Santa Cruz), CD68 (1:500, MCA1957GA, Bio-Rad), TREM2 (1:50, AF1729, R and DSystems), APP-Y188 (1:2000, ab32136, Abcam), CLEC7a (1:50, mabg-mdect, Invivogen), TMEM119 (1:200, ab209064, Abcam), APOE-biotinylated (HJ6.3, 1:100, [Kim et al., 2012]), 3552 (1:5000, [Yamasaki et al., 2006]) and pE3-Aβ (J8, 1:500, [Hartlage-Rübsamen et al., 2018]). pE3-Aβ immunostaining required heat antigen retrieval (25 min at 95°C) in citrate buffer (10 mM, pH 6.0) prior blocking. After primary antibody incubation, brain sections were washed 3 times with PBS-T and incubated with appropriate fluorophore-conjugated or streptavidine-fluorophore conjugated (for APOE biotinylated antibody) secondary antibodies (1:500, Life Technologies) together with nuclear stain Hoechst 33342 (1:2000, H3570, Thermo Fisher Scientific), for 2 hr at room temperature (RT). Fibrillar dense core plaques were stained with ThR (Sigma Aldrich, 2 µM solution in PBS) for 20 min in the dark at RT (after secondary antibody staining). Sections were subsequently washed 3 times with PBS-T, mounted onto glass slides (Thermo Scientific), dried in the dark for at least 30 min, mounted using Gel Aqua Mount media (Sigma Aldrich) and analyzed by confocal microscopy.

3 month old APPPS1 and APP-KI mice were analyzed for dystrophic neurites, plaque size, microglial recruitment and CD68 and total Aβ coverage. pE3-Aβ coverage was analyzed in 12 month old APPPS1 and APP-KI mice. All quantification analysis were performed in a blinded manner and included at least three mice per genotype. Mice sex and numbers of biological and technical replicates for all immunohistological experiments are summarized in. Supplementary file 5

For the analysis of microglial recruitment, plaque and dystrophic neurite size and CD68 coverage, 30 z-stack images (10–12 µm thick) of randomly selected plaques from neocortical regions were acquired per experiment using a confocal microscope (63X water objective with 2x digital zoom, 600 Hz, Leica TCS SP5 II). Microscopy acquisition settings were kept constant within the same experiment. Maximal intensity projection pictures from every z-stack were created using ImageJ software and for every image, a defined region of interest (ROI) was manually drawn around every plaque (including microglia recruited -in contact- to the plaque). APP (Y188 antibody) and CD68 coverage area were quantified using the ‘Threshold’ and ‘Analyze Particles’ (inclusion size of 1-Infinity) functions from ImageJ software (NIH) within the ROI. The area covered by CD68 was normalized to the total Aβ plaque area (NAB228 antibody) or was divided by the number of microglia (IBA1 positive cells) recruited to the plaque within the ROI. The absolute values of area covered by neuritic dystrophies or Aβ plaques are represented in square micrometers (µm²). Microglial recruitment to plaques was quantified by counting the number of microglia (IBA1 positive cells) around amyloid plaques through the z-stack images within the defined ROI using the cell counter function of ImageJ software. Number of microglial cells at amyloid plaques was normalized to the area covered by Aβ (NΑΒ228 antibody) or by dystrophic neurites (APP, Y188 antibody) and expressed as number of microglial cells per µm² of Aβ plaque.

For the quantification of total Aβ and pE3-Aβ coverage at 3 and 12 months of age, respectively, 18 images were systematically taken from comparable neocortical regions using a confocal microscope (20X dry objective, 600 Hz, Leica TCS SP5 II). Quantification of Aβ and pE3-Aβ areas was performed using a self-programmed macro with ImageJ software outlined in, respectively. Supplementary file 6A and B

Representative images from microglial recruitment analysis (IBA1 positive cells and CD68 coverage) were taken using the confocal microscope (63X water objective with 2x digital zoom, 400 Hz, Leica TCS SP5 II). Representative images of microglia polarized towards amyloid cores and of microglial recruitment and dystrophic neurite size were taken using a 63X confocal water objective with 3x digital zoom.

For immunohistological validation of the proteome and analysis of amyloid pathology, representative images from comparable neocortical regions were taken by confocal microscopy using the same settings for all three different genotypes (WT, APPPS1 and APP-KI). Low magnification pictures were taken with 20X dry confocal objective with 2x digital zoom and higher magnification ones with 63X confocal water objective with 3x digital zoom (400 Hz, Leica TCS SP5 II). Images of Aβ pathology (NΑΒ228 antibody) were taken with a tile scan system covering comparable cortico-hippocampal regions (10X confocal dry objective, 400 Hz, Leica TCS SP5 II). Representative images of Aβ composition and microglia (NAB228, ThR and IBA1) and pE3-Aβ were taken with a confocal 20X dry objective (400 Hz, Leica TCS SP5 II).

3 and 12 month old APPPS1 and APP-KI mice were analysed using LCOs. Information including mice sex and biological and technical replicates is outlined in Supplementary file 5. LCO analysis was performed as previously described (Rasmussen et al., 2017). Briefly free-floating brain sections from PFA-perfused mice (as described in Immunohistochemistry) were incubated with two LCOs, qFTAA and hFTAA (2.4 µM qFTAA and 0.77 µM hFTAA in PBS), for 30 min at RT in the dark and subsequently washed 3 times with PBS-T. Sections were mounted, air-dried and mounted with Dako mounting medium. Randomly chosen images from neocortex (4–10 images per mouse) were acquired with a confocal 40X oil objective on a Zeiss LSM 510 META confocal microscope equipped with an argon 458 nm laser for excitation and a spectral detector. Emission spectra were acquired from 470 to 695 nm and values of each plaque were normalized to their respective maxima. For each mouse, 20 plaques were randomly chosen from the images, normalized values were averaged for each mouse and the mean was taken for each mouse line (spectrum). The ratio of the intensity of emitted light at the blue-shifted peak (502 nm from qFTAA) and red-shifted peak (588 nm from hFTAA) was used as a parameter for spectral distinction of different Aβ conformations in plaque cores.

Microglial phagocytosis was performed similarly as previously described (Kleinberger et al., 2014). Microglia isolated from 3 or 6 month old APPPS1, APP-KI and WT mice were plated onto 24 well plate at a density of 2 × 10⁵ cells per well and cultured for 24 hr in a humidified 5% CO₂ incubator at 36.5°C in DMEM/F12 media (Invitrogen) supplemented with 10% heat inactivated FCS (Sigma), 1% Penicillin-Streptomycin (Invitrogen) and 10 ng/mL GM-CSF (R&DSystems). After 24 hr, plating media were replaced with fresh media. After 5 days in culture, microglia were incubated with 50 µL of E. coli particle suspension (pHrodo Green E. coli BioParticles, P35366, Invitrogen) for 60 min. Cytochalasin D (CytoD, 10 µM, from 10 mM stock in DMSO) was used as phagocytosis inhibitor and added 30 min prior to addition of bacterial particles. Bacteria excess was washed four times with PBS (on ice) and microglia attached to the plate were incubated with CD11b-APC-Cy7 antibody (1:200, clone M1/70, 557657, BD) in FACS buffer (PBS supplemented with 2 mM EDTA and 1% FBS) for 30 min at 4°C. Microglia were then washed twice with PBS, scraped off in FACS buffer and analyzed by flow cytometry. Information including mice sex and biological and technical replicates is outlined in Supplementary file 7.

For the microglial isolation quality control, around 12000 cells from a CD11b-enriched and CD11b-depleted fractions were stained in suspension with CD11b-APC-Cy7 antibody (1:200, clone M1/70, 557657, BD) in FACS buffer for 30 min at 4°C. After several washes with PBS, microglia were resuspended in FACS buffer for analysis. Propidium Iodide (PI) staining was done 10 min prior to FACS analysis. Flow cytometric data was acquired on a BD FACSverse flow cytometer by gating according to single stained and unstained samples and analyzed using FlowJo software (Treestar). Mean fluorescent intensity (MFI) is represented as the geometric mean of the according fluorochrome.

Immunohistochemical and FACS results are presented as mean ± standard deviation (± SD) from at least three independent experiments with the exception of the phagocytic assay at 6 months where two independent experiments were performed (Figure 9J and K). Statistical significance (p-value) was calculated using the unpaired two-tailed Student's t-test. Phagocytic assay was analyzed by the Dunnett’s multiple comparison test of the Two-way ANOVA. Both statistical analysis were performed in GraphPad Prism. P-value of <0.05 was considered statistically significant (*; p<0.05, **; p<0.01, ***; p<0.001 and ****; p<0.0001, n.s.=not significant).

The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository (Perez-Riverol et al., 2019) with the dataset identifier PXD016075.