Abstract
Microglial dysfunction is a key pathological feature of Alzheimeŕs disease (AD), but little is known about proteome-wide changes in microglia during the course of AD pathogenesis and their functional consequences. Here, we performed an in-depth and time-resolved proteomic characterization of microglia in two mouse models of amyloid β (Aβ) pathology, the overexpression APPPS1 and the knock-in APP-NL-G-F (APP-KI) model. We identified a large panel of Microglial Aβ Response Proteins (MARPs) that reflect a heterogeneity of microglial alterations during early, middle and advanced stages of Aβ deposition. Although both mouse models display severe microglial alterations at late stages of amyloid pathology, MARP signatures occur earlier in the APPPS1 mice. Strikingly, the kinetic differences in proteomic profiles correlated with the presence of fibrillar Aβ, rather than dystrophic neurites, suggesting that fibrillar Aβ aggregates are the main drivers of the AD-associated microglial phenotype and the observed functional decline. The identified microglial proteomic fingerprints of AD provide a valuable resource for functional studies of novel molecular targets and potential biomarkers for monitoring AD progression or therapeutic efficacy.
Introduction
Microglia play fundamental roles in a variety of neurodegenerative diseases, including AD (McQuade and Blurton-Jones, 2019). Changes in brain immunity, together with extracellular Aβ deposition and neurofibrillary tangles, are major pathological culprits in AD (Gjoneska et al., 2015; Guillot-Sestier and Town, 2013; Holtzman et al., 2011; Shi and Holtzman, 2018). The importance of microglia in AD pathogenesis is well illustrated by the increasing number of identified AD risk genes which are expressed in microglia and have functions in brain immunity (Cuyvers and Sleegers, 2016; Guerreiro et al., 2013; Jansen et al., 2019; Jonsson et al., 2013; Karch and Goate, 2015; Lambert et al., 2009; Naj et al., 2011; Sims et al., 2017). For example, the triggering receptor expressed on myeloid cells 2 (Trem2) and apolipoprotein E (ApoE) are major genetic risk factors for sporadic AD that are expressed by plaque-associated microglia and involved in Aβ clearance (Bradshaw et al., 2013; Castellano et al., 2011; Kleinberger et al., 2014; Parhizkar et al., 2019; Reddy et al., 2009; Wang et al., 2015). It has also been shown that microglial phagocytosis decays over the course of AD (Hickman et al., 2008; Koellhoffer et al., 2017; Orre et al., 2014a; Solito and Sastre, 2012; Zuroff et al., 2017). Along these lines, Aβ clearance was found reduced in sporadic AD and it is assumed to be a key factor in the pathogenesis (Mawuenyega et al., 2010; Saido, 1998; Wildsmith et al., 2013). Importantly, Aβ clearance defects in AD microglia are reversible (Daria et al., 2017) and enhancing microglial phagocytic function has been explored as a therapeutic approach since substantial reduction of Aβ burden appears to correlate with cognitive benefits (Bacskai et al., 2001; Bard et al., 2000; Bohrmann et al., 2012; Janus et al., 2000; Lathuiliere et al., 2016; Morgan et al., 2000; Nicoll et al., 2006; Nicoll et al., 2003; Schenk et al., 1999; Schilling et al., 2018; Sevigny et al., 2016; Wilcock et al., 2004). However, when and how microglia change along AD progression is still not clear. Thus, understanding molecular alterations of microglia at different stages of AD is crucial and a pre-requisite for developing safe and efficacious therapy.
Transcriptional expression profiles for microglia were previously revealed under physiological, neurodegenerative or neuroinflammatory conditions (Butovsky et al., 2014; Galatro et al., 2017; Gosselin et al., 2017; Gotzl et al., 2019; Grabert et al., 2016; Holtman et al., 2015; Kamphuis et al., 2016; Krasemann et al., 2017; Mazaheri et al., 2017; Orre et al., 2014a; Orre et al., 2014b; Wang et al., 2015; Yin et al., 2017). Transcriptional signatures were also recently reported at single-cell resolution, demonstrating regional and functional heterogeneity of brain myeloid cells (Hammond et al., 2019; Jordao et al., 2019; Keren-Shaul et al., 2017; Mathys et al., 2017; Sala Frigerio et al., 2019). In neurodegenerative mouse models, two major profiles have been proposed along the spectrum of microglial alterations. One is the homeostatic microglial signature that occurs under physiological conditions and is characterized by the expression of several genes, including P2ry12, Tmem119 and Cx3cr1. The other key signatures, referred to as disease-associated microglia (DAM), microglial neurodegenerative phenotype (MGnD) or activated response microglia (ARM) are observed under neurodegenerative conditions (Keren-Shaul et al., 2017; Krasemann et al., 2017; Sala Frigerio et al., 2019) and characterized by increased expression of ApoE, Trem2, Cd68, Clec7a and Itgax (Cd11c), among others. These changes were quantified using RNA transcripts, but transcript levels do not necessarily reflect protein levels which ultimately control cell function (Bottcher et al., 2019; Mrdjen et al., 2018; Sharma et al., 2015). Importantly, a recent study postulated that transcriptomic profiles of microglia from another AD mouse model (5xFAD) do not correlate well with proteomic changes (Rangaraju et al., 2018), suggesting the existence of additional translational or post-translational regulation mechanisms in AD microglia. Additionally, little is known about Aβ-associated changes in the microglial proteome in a time-resolved manner, or which proteome alterations underscore microglial dysfunction. Accordingly, we analyzed the microglial proteome at distinct stages of Aβ pathology in two commonly used mouse models of amyloidosis; the APPPS1 (Radde et al., 2006), and the APP-KI mice (Saito et al., 2014). In contrast to the APPPS1 mouse model that overexpresses mutated human amyloid precursor protein (APP) and presenilin-1 (PS1), the APP-KI model bears endogenous levels of APP with a humanized Aβ sequence containing three AD mutations (NL-G-F), and has no alterations of PS1 (Radde et al., 2006; Saito et al., 2014).
Our study determines the proteome of microglia from APPPS1 and APP-KI mice in a time resolved manner, starting from pre-deposition to early, middle and advanced stages of amyloid deposition and reveals a panel of MARPs that progressively change throughout Aβ accumulation. Although both mouse models display severe microglial alterations at late stages of Aβ pathology, the occurrence of MARP signatures differs and appears earlier in the APPPS1 mice. Strikingly, the kinetic differences in proteomic profiles correlated with the presence of fibrillar Aβ, rather than dystrophic neurites, suggesting that fibrillar Aβ aggregates are the main drivers of the AD-associated microglial phenotype and corresponding functional decline. The time-resolved microglial profiles may serve as benchmark proteomic signatures for investigating novel microglial targets or monitoring the efficacy of future pre-clinical studies aiming at microglial repair.
Results
APPPS1 microglia develop an AD-associated proteomic signatures earlier compared to the APP-KI microglia
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 a 97% of isolated cells were CD11b positive (Suppl. Fig 1A). Of note, only 0.49% of CD11b positive cells were detected in the CD11b-depleted fraction (Suppl. Fig 1B), demonstrating high isolation efficiency. Next, we optimized the data acquisition method for microglial proteome analysis (Suppl. Fig 2A; Suppl. Table 1). Recently, it was shown that Data Independent Acquisition (DIA) for label-free quantification (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 (Suppl. Table 2) 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 (Suppl. Table 2). 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 29% increase of relatively quantified proteins from 4412 with DDA to 5699 with DIA (Suppl. Table 2; Suppl. Fig 2B and C). We therefore selected DIA for further proteomic characterization of APPPS1 and APP-KI microglia. Notably, we also detected a consistent relative quantification of proteins with an overlap of 93.5% between the two mouse models (Suppl. Fig 2D), supporting our selection of DIA as a robust method for microglial proteomic analysis.
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 (Suppl. Fig 2A). For our proteomic analysis, we have set as a threshold a log2 fold change larger than 0.5 or smaller than −0.5 compared to the WT 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 (Suppl. Fig 3). At the pre-deposition stage (1 month), microglial proteomes of APPPS1 and APP-KI mice did not show significant alterations compared to WT (Fig 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 (Fig 1C; Suppl. Table 3A). In contrast, APP-KI microglia were hardly affected at 3 months of age (Fig 1D; Suppl. Table 3B), which is particularly surprising because both mouse models show comparable amyloid burden at this stage (Suppl. Fig 3). At 6 months of age, microglia in APPPS1 mice displayed 309 up-regulated and 261 down-regulated proteins, compared to WT microglia (Fig 1E; Suppl. Table 3A). In contrast to 3 months of age (Fig 1D), APP-KI mice at 6 months of age displayed a substantial alteration of their microglial proteome, illustrated by 140 up-regulated and 151 down-regulated proteins (Fig 1F; Suppl. Table 3B). Still, microglial alterations in 6 month old APP-KI mice were less pronounced compared to the proteome of APPPS1 mice (Fig 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 (Fig 1G and H; Suppl. Table 3A and B). This indicates comparable changes in APPPS1 and APP-KI mice at advanced stages of Aβ pathology. 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.
Identification of MARPs as signatures of early, middle and advanced amyloid stages
Next, we determined protein alterations that first appear in early, middle or advanced stages of Aβ deposition and remain altered thought 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 thus provides a better time resolution of protein alterations to amyloid response, compared to the APP-KI model (Fig 2A). Correspondingly, we defined early (proteins changed at 3, 6, and 12 months), middle (proteins changed only at 6 and 12 months) and advanced (proteins changed only at 12 months) MARPs. 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 (Suppl. Fig 4A). The most strongly regulated MARPs with early, middle and advanced response are displayed in corresponding heatmaps (Fig 2B-D). In addition, we compared MARP signatures with the previously delineated RNA signatures of 5xFAD mice (Keren-Shaul et al., 2017) to visualize the overlap, as well as differences, between proteomic and transcriptomic microglial profiles.
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 (Fig 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 (Fig 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 (Fig 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 (Fig 3A; Suppl. Fig 5A und D). Thus, these processes represent first molecular alterations that progressively follow Aβ plaque pathology.
The middle MARPs included the up-regulated proteins FABP3, FABP5, CD63, TREM2, MIF and GUSB (Fig 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 (Fig 2C). Among the down-regulated middle MARPs, we identified additional chemotaxis and cell migration related proteins like SYK, FER, CX3CL1, and BIN2 (Fig 2C; Suppl. Table 4), underscoring a loss of key homeostatic functions of microglia throughout AD progression.
Advanced MARPs represent proteins that were only altered upon extensive amyloid pathology and show a high correlation between the two models (Suppl. Fig 4B). 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 (Fig 2D; Suppl. Table 4). In addition, different G protein-coupled receptor signaling proteins, including GNG2, GNG5 and GNG10, also displayed a decreased abundance (Fig 2D).
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 (Fig 3B and C; Suppl. Fig 5B, C, E and F). Conversely, we found an up-regulation of protein glycosylation and carbohydrate metabolism (Fig 3C; Suppl. Fig 5E and F). Additionally, alterations in ion transport processes involving ion homeostasis and pH regulation were also detected (Fig 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 (Suppl. Table 5A and B).
The overlap of consistently quantified proteins and a previously published transcriptome study (Keren-Shaul et al., 2017) was 38.4%, whereas 2152 and 2841 gene products were only quantified on protein and transcript level, respectively (Suppl. Fig 4C). Single cell transcriptomics (Keren-Shaul et al., 2017) has demonstrated a similar regulation of a number of early MARPs while we found less overlap for middle and advanced MARPs (Fig 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 (Fig 2B-D; Suppl. Table 4). Furthermore, the advanced MARP HEXB showed a consistent up-regulation in our proteomic analysis, despite being previously anticipated as a homeostatic gene (Suppl. Table 4).
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.
Proteomic changes are detected in plaque-associated microglia
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 middle MARP CSF1R (Suppl. Fig 4D) 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 (Fig 4), TREM2 (Suppl. Fig 6) and APOE (Suppl. Fig 7) 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 MARPs such as CLEC7a (Fig 5) and decreased levels of TMEM119 (Fig 6) 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.
APPPS1 and APP-KI mice show similar dynamics of amyloid plaque deposition, but differ in plaque fibrillization
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 (Fig 1C and D; Fig 2A) despite the comparable plaque load observed in both mouse models (Suppl. Fig 3). 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 amyloid plaques, and Thiazine red to visualize fibrillar amyloid plaque cores (Daria et al., 2017) (Fig 7A). 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 (Fig 7A). The amount of fibrillar Aβ in APP-KI mice increased at 6 and 12 months, but overall still remained lower compared to the APPPS1 mice. This result was also confirmed by biochemical analysis in which fibrillar Aβ was specifically detected via immunoblot of the insoluble brain fraction (Fig 7B). Therefore, we excluded differences in the detection and binding properties of Thiazine red to be the underlying cause for the observed reduction in the levels of fibrillar Aβ in APP-KI mice. Taken together, although immunohistochemistry revealed comparable Aβ plaque coverage in APPPS1 and APP-KI mice, the amount of fibrillar Aβ was significantly lower in APP-KI mice.
Microglial recruitment is triggered by fibrillar Aβ and not by dystrophic neurites
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 (Fig 1C and D; Fig 2A) as well as in the amount of fibrillar Aβ (Fig 7A and B) between the two AD mouse models. Immunohistochemical analysis revealed IBA1 positive, amoeboid microglia recruited to large, Thiazine red positive, fibrillar Aβ plaque cores in APPPS1 mice. Of note, we observed intracellular fibrillar Aβ in APPPS1 microglia in close contact to the plaque core (Fig 8A) as previously reported (Bolmont et al., 2008). Despite the significantly smaller fibrillar Aβ plaque core 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 (Fig 8A). Quantification analysis revealed increased clustering of IBA1 positive microglia around Aβ plaques in APPPS1 compared to the APP-KI mice (Fig 8B), which display overall larger Aβ plaque size (Fig 8C). Likewise, we observed increased CD68 immunoreactivity around Aβ plaques in the APPPS1 compared to the APP-KI mice (Fig 8D and E). However, CD68 signal per individual microglial cell in the plaque vicinity was similar in both models (Fig 8F), 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.
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 (Fig 8G). Interestingly, despite the reduced load of fibrillar Aβ, we readily detected dystrophic neurites in the APP-KI mice (Fig 8G). Moreover, our quantification analysis revealed a trend towards an increased dystrophic neurite area in the APP-KI compared to the APPPS1 mice (Fig 8H). 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 is primarily triggered by the fibrillar Aβ content of amyloid plaques which drives the acquisition of MARP signatures.
Phagocytic impairments correlate with the occurrence 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 (Gotzl et al., 2019; Kleinberger et al., 2014). We already detected phagocytic dysfunction in 3 month old APPPS1 microglia, which was reflected by a prominent decrease in the amount of intracellular E.coli particles (Fig 8I; Suppl. Fig 8A) and a reduced number of CD11b positive cells that were capable of E.coli uptake (Fig 8J; Suppl. Fig 8B). 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 (Fig 8I and J). Overall, we observed different kinetics of microglial dysfunction among mouse models which correlate with the appearance of MARPs and, in turn, with the presence fibrillar Aβ.
Discussion
This study presents an in-depth and time-resolved proteome of microglia isolated across different stages of Aβ accumulation in the APPPS1 and APP-KI mouse models, resulting in the identification of early, middle, and advanced MARPs. Importantly, we demonstrated that the structure of amyloid plaques (fibrillar versus non-fibrillar) is a major determinant driving the molecular alterations of microglia. Key microglial signatures encompass proteins with a central function in microglial biology and AD pathogenesis. Moreover, our functional analysis shows that early MARP signatures already reflect microglial phagocytic dysfunction.
To achieve robust and reproducible relative quantification of microglial proteins from single mice, we improved the yield of acutely isolated microglia to an average of 2×106 cells per mouse brain, compared to recently published protocols (Flowers et al., 2017; Rangaraju et al., 2018). Next, by establishing the more sensitive DIA method for protein quantification, we improved the number of consistently identified proteins by 29.3% and obtained on average 5699 (APPPS1) and 5698 (APP-KI) relatively quantified proteins. Notably, our analysis enhanced the detection of low abundance proteins and does not require data imputation. The advancement to previous studies (Rangaraju et al., 2018; Sharma et al., 2015) is also exemplified by quantification of membrane proteins, including well known microglial homeostatic markers TMEM119 or P2RY12. We also measured alterations in proteins that were postulated to be only altered at the transcriptional level in AD microglia (Rangaraju et al., 2018), including up-regulation of middle MARPs FABP3, FABP5, MIF and PLP2. In summary, our study achieved a major improvement in quantitative proteomic analysis of rodent microglia (Flowers et al., 2017; Rangaraju et al., 2018; Thygesen et al., 2018). This methodological advance enabled us to map microglial changes across diverse stages of Aβ pathology in two widely explored pre-clinical models of amyloidosis. Generated proteomic profiles characterize microglia under diseased conditions and can be used as a resource to track changes upon microglial therapeutic modification, such as Aβ immunotherapy. Such studies would facilitate discovery of clinically relevant molecular alterations that are necessary for microglial functional repair, monitoring disease progression and therapeutic efficacy.
The TREM2/APOE axis plays a key role in the regulation of the microglial transcriptional program and guides the homeostatic/DAM signature switch (Jay et al., 2017; Keren-Shaul et al., 2017; Krasemann et al., 2017). Our time-resolved proteomic analysis observed major rearrangements of the microglial proteomic landscape in both APPPS1 and APP-KI mice and revealed a partial overlap between MARPs and transcriptional profiles of DAM and homeostatic microglia (Keren-Shaul et al., 2017), but also identified additional microglial marker proteins throughout different stages of Aβ deposition.
Early MARPs include proteins of the interferon response, which is consistent with the recently identified interferon-responsive microglial sub-population in AD mice (Sala Frigerio et al., 2019). Numerous up-regulated early MARPs, including CD74, CTSZ, HEXA, CTSH, GLB1, CD68, NPC2, CLN3 and PI4K2A, reflect alterations in endo-lysosomal homeostasis as an early pathological insult in AD microglia (Van Acker et al., 2019). Additionally, factors of the fatty acid and cholesterol metabolism are altered throughout all pathological phases. Up-regulated are the early (APOE, ACACA, and SOAT1) middle (FABP3, FABP5, NCEH1, APOD, AACS, ACOX3, HACD2) and advanced MARPs (ACOT11, ACSBG1, ECHS1, ELOVL1, and FASN) and down-regulated are several middle and advanced MARPs (NAAA, FAM213B, HPGD, HPGDS, and PRKAB1), linking microglial lipid dyshomeostasis and AD pathology.
An inflammatory response in AD is suggested by the significant up-regulation of early MARPs LGALS3 and its binding protein (LGALS3BP). Recent findings suggested that the LGALS3/TREM2 signalling pathway, that acts as an inflammatory regulator of amyloid plaque formation, may also be of relevance for AD pathology in humans (Boza-Serrano et al., 2019). Further evidence that some of the presented proteomic alterations of rodent microglia may be relevant for human disease is given by the detection of up-regulated early/middle microglial MARPs, including CD68, TREM2 and ITGAX in microglia surrounding amyloid plaques in postmortem AD brains (Hopperton et al., 2018). As microglia emerge as a promising therapeutic target in AD, additional MARP signatures should be validated in human tissue. In particular, early MARPs that are strongly increased in both AD mouse models may serve as a resource to identify novel AD biomarkers and more specific microglial positron emission tomography (PET) tracers that are urgently needed to monitor microglial reactivity in vivo (Edison et al., 2018; Hemonnot et al., 2019). Middle and late MARPs reveal a decrease of microglial homeostatic functions affecting chemotaxis, cell migration and phagocytosis (e.g., CX3CR1, SYK, P2RY12, BIN2, TFEB and TFE3) and thus mark AD progression.
It is still being discussed which is the main trigger for microglial recruitment to amyloid plaques and their molecular switch from a homeostatic to a neurodegenerative phenotype (Hemonnot et al., 2019; Jung et al., 2015; Krasemann et al., 2017). Our study proposes that microglial recruitment to Aβ deposits and their corresponding disease-associated proteomic alterations are triggered by fibrillar Aβ, rather than by dystrophic neurites. We observed more diffuse amyloid plaque morphology with less fibrillar Aβ and prominent neuritic dystrophies in the APP-KI mice. Similar plaque morphology, with less fibrillar Aβ, is also observed in AD mice deficient for TREM2 or APOE that also have less microglial cells recruited to amyloid plaques and display prominent neuritic dystrophies (Parhizkar et al., 2019; Sala Frigerio et al., 2019; Ulrich et al., 2018; Wang et al., 2015; Yuan et al., 2016). APOE may have a dual role and control the transcriptional/translational response of microglia to amyloid as well as amyloid plaque compactness that directs microglial recruitment and thus creates a regulatory feedback-loop. These findings are also strengthened by the relevance of ApoE and Trem2 as genetic risk factors of AD (Karch and Goate, 2015). Fibrillar Aβ as the trigger for microglial recruitment is also supported by the human pathology where neuritic plaques in AD brains were found surrounded by microglia. In contrast, microglial clustering was not detected at diffuse plaques lacking fibrillar Aβ core (D’Andrea et al., 2004).
Although DAM signatures have been suggested as a protective response, there is still a lack of direct experimental evidence linking specific transcriptomic or proteomic profiles to improved microglial function. Importantly, our study demonstrates a functional link between proteomic changes and reduced phagocytosis by AD microglia. APPPS1 microglia start acquiring early MARPs at the age of 3 months, which is already accompanied by reduced phagocytic function. In contrast, less altered proteomic signatures of 3 month old APP-KI microglia correlated with preserved phagocytic function. Pronounced MARP signatures that appeared later in APP-KI microglia (6 months) were subsequently in accordance with phagocytic impairments. Therefore, differences in plaque fibrillization in both mouse models did not only affect microglial recruitment and activation, but also the phagocytic function of microglia.
Reduced phagocytosis of AD microglia might be related to observed proteomic alterations in lysosomal proteins or cell receptors. TREM2, which was found to be increased in both mouse models, plays an important role in phagocytosis as mutations of TREM2 related to AD and FTLD impair phagocytic activity of microglia (Kleinberger et al., 2014). However, up-regulation of the TREM2/APOE axis involves up-regulation of many lysosomal proteins (e.g., cathepsins or CD68) that are part of MARPs and altered in APPPS1 and APP-KI microglia. This may reflect a compensatory mechanism initiated as a response of microglia to Aβ accumulation in order to enhance phagocytic function. Eventually this frustrated microglial response fails to translate into improved Aβ clearance capability.
Phagocytosis might also be altered through differential regulation of toll like receptors (TLR). Among the TLRs, TLR2, an Aβ binding receptor (Liu et al., 2012; McDonald et al., 2016), showed the strongest increase with age while TLR9 was significantly reduced in APPPS1 and APP-KI mice. Along these lines, TLR2 deficiency reduced the inflammatory response of microglia to Aβ42, but increased Aβ phagocytosis in cultured microglia (Liu et al., 2012) while TLR9 is associated with improved Aβ clearance (Scholtzova et al., 2009). Thus, differential regulation of TLRs might contribute to the reduced phagocytic activity of aged APPPS1 microglia (Daria et al., 2017).
Additionally, many purinergic receptors (e.g., P2RX7, P2RY12 or P2RY13), which are important regulators of chemotaxis, phagocytosis, membrane polarization, and inflammatory signaling and thus emerged as possible microglial targets in AD (Calovi et al., 2019; Hemonnot et al., 2019), were found to be down-regulated in both AD mouse models. P2RY12 is regarded as a marker for ramified non-inflammatory microglia (Mildner et al., 2017) that is reduced in response to Aβ plaques and therefore represents a homeostatic microglial marker (Keren-Shaul et al., 2017; Krasemann et al., 2017). In contrast, P2RX4, a purinergic receptor that is likely to be involved in shifting microglia towards a pro-inflammatory phenotype (Calovi et al., 2019) or myelin phagocytosis (Zabala et al., 2018) had an increased abundance in both AD models. Taken together, our data emphasize alterations of purinergic receptor signaling in AD microglia that may regulate a morphological change towards amoeboid microglia with reduced motility and increased pro-inflammatory activity.
Our study confirms that both mouse models are valuable tools for studying Aβ−induced pathological changes of microglia that are remarkably comparable at advanced stages of amyloidosis. However, the observed differences in the dynamics of early, middle and late MARPs in APPPS1 and APP-KI mice should be considered for the design of pre-clinical studies of microglial repair and will require different time windows for microglial modulation.
In conclusion, we tracked pathological alterations of microglia in two AD mouse models using a proteomic approach. Our work demonstrates that microglial alterations are triggered as a response to Aβ deposition as pre-deposition stages do not reveal proteomic alterations. The conversion to MARPs is supported by changes in TREM2-APOE regulation mechanism. AD microglia display pronounced interferon stimulation, increased antigen presentation, alterations in cell surface receptors, lipid homeostasis and metabolism. Those proteomic changes in microglia occur as a response to fibrillar Aβ and are reflected in amoeboid microglial morphology and impaired phagocytic capacity. Finally, our proteomic dataset serves a valuable research resource providing information on microglial alterations over different stages of Aβ deposition that can be used to monitor therapeutic efficacy of microglial repair strategies.
Materials and Methods
Animals
Male and female mice of the hemizygous APPPS1 mouse line overexpressing human APPKM670/671NL and PS1L166P under the control of the Thy-1 promoter (Radde et al., 2006), homozygous AppNL-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 h 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.
Isolation of primary microglia
Primary microglia were isolated from mouse brains (cerebrum) using MACS Technology (Miltenyi Biotec) according to manufactureŕs instructions and as previously described (Daria et al., 2017). Briefly, olfactory bulb, brain stem and cerebellum were removed and the remaining tissue (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 3 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.
Sample preparation for mass spectrometry
Microglia-enriched pellets were 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 × 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) (Wisniewski 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 h at 37°C followed by a second digestion step with 0.15 µg trypsin (Promega) for 4 h 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).
Liquid chromatography – tandem mass spectrometry analysis
For 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 × 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; 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; 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+6 ions; Suppl. Table 1). The maximum ion trapping time was set to “auto”. A stepped normalized collision energy of 26% ± 2.6% was used for fragmentation.
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.
Mass spectrometric LFQ and data analysis
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 1st 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 wild type 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. Additionally, a permutation based FDR estimation was used (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. 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 da et al., 2009a, b) 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.
Biochemical characterization of brain tissue and isolated microglia
RIPA lysates were prepared from brain hemispheres, centrifuged at 100000 × 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 (10-20%, Thermo Fisher Scientific) gels, 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 hour, 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 proteinase and phosphatase inhibitors), kept on ice for 20 min and then sonicated for 4 cycles of 30 seconds. Cell lysates were then centrifugated at 9600 × g (5 min. at 4°C) and pellets discarded. Protein concentration was quantified using Bradford assay (Biorad) according to manufacturer instructions. 10 µg per sample using 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&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.
Immunohistochemistry
3 and 12 month old mice from the APPPS1 and APP-KI transgenic lines 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 minutes followed by 4% Paraformaldehyde (PFA) in 0.1 M PBS for 15 minutes. Brains were isolated and postfixed for 20 minutes 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 (O.C.T./ Tissue-Tek, 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 hour 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, AbDserotec), TREM2 (1:50, AF1729, R&DSystems), APP-Y188 (1:2000, ab32136, Abcam), CLEC7a (1:50, mabg-mdect, Invivogen), TMEM119 (1:200, ab209064, Abcam), APOE-biotinilated (HJ6.3, 1:100, (Kim et al., 2012)) and 3552 (1:5000, (Yamasaki et al., 2006)). 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,ThermoFisher), for two hours at room temperature (RT). Fibrillar dense core plaques were stained with Thiazine red (Sigma Aldrich, 2 µM solution in PBS) for 20 min in the dark at RT (after secondary antibody staining). Sections were subsequently washed three 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.
Image acquisition, analysis and quantifications
3 month old APPPS1 and APP-KI mice were used for the analysis of dystrophic neurites, microglial recruitment to amyloid plaques and CD68 coverage area. All quantification analysis included 3 mice per genotype. 30 z-stack images (∼10 µm thick) of single cortical plaques were acquired per experiment using a confocal microscope (63X water objective with 2x digital zoom, 600 Hz, Leica TCS SP5 II) from 6 brain slices (5 plaques per slice) for the microglial recruitment and dystrophic neurite analysis, or from 3 brain slices (10 plaques per slice) for CD68 coverage area analysis. 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 (NAB228 antibody) and expressed as number of microglial cells per µm2 of Aβ plaque.
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 picture of microglia polarized towards amyloid cores was taken using a 63X confocal water objective with 3x digital zoom.
For immunohistological validation of the proteome analysis and amyloid pathology, representative pictures in similar 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. Images of Aβ pathology (NAB228 antibody) were taken with a tile scan system covering similar brain regions (10X confocal dry objective). Representative images of Aβ composition (NAB228, ThR and IBA1) were taken with a confocal 20X dry objective.
Microglial phagocytosis of E.coli particles
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 hours 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 hours, 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, from10 mM stock in DMSO) was used as phagocytosis inhibitor and added 30 min prior to addition of bacterial particles. Bacteria suspension excess was washed 4 times with PBS (on ice) and microglia that were attached to the plate were incubated with CD11b-APC-Cy7 antibody (1:200, clone M1/70, 557657, BD) in FACS buffer (PBS supplemented with 2mM EDTA and 1% FBS) for 30 min at 4°C. Microglia were then washed twice with PBS, scraped off the wells in FACS buffer and analyzed by flow cytometry. For the analysis of 3 month old mice, 3 independent experiments were performed per genotype, and each experiment included a minimum of 3 technical replicates with the exception of CytoD condition (2 technical replicates). For the analysis of 6 month old mice, 2 independent experiments were performed per genotype, and each experiment included a minimum of 4 technical replicates with the exception of CytoD condition (2 technical replicates).
FACS analysis
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 minutes at 4°C. After several washes with PBS, microglia were resuspended in FACS buffer for analysis. Propidium Iodide (PI) staining was done 10 minutes prior 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.
Statistical analysis
The data are presented as mean ± standard deviation of the mean (± SD) from 3 independent experiments with the exception of the phagocytic assay in 6 month old mice (Fig 8) were 2 independent experiments were performed. For the microglial recruitment and analysis of dystrophic neurites, 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 to be statistically significant (*; P < 0.05, **; P < 0.01 and ***; P < 0.001, n.s. = not significant).
Data Availability
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.
Author Contributions
S.T. and S.F.L. designed and supervised the study. S.T., S.F.L., S.A.M., L.S.M. wrote the manuscript with input of all co-authors. L.S.M. performed animal experiments including microglial isolation, target validation and functional studies. A.C. assisted in isolation of primary microglia. S.A.M., J.K., and A.B. performed the proteomic analysis. S.R., L.S.M. and A.L. performed FACS analysis. T.S. and T.C.S. provided the APP-KI mouse model. J.H. contributed to amyloid plaque analysis. L.S.M., M.W., and C.H. contributed to biochemical analysis. Correspondence and requests for materials should be addressed to S.T. or S.F. L.
Competing Interests
C.H. collaborates with Denali Therapeutics, participated on one advisory board meeting of Biogen, and received a speaker honorarium from Novartis and Roche. C.H. is chief advisor of ISAR Bioscience. All other authors declare that they have no competing interests.
Acknowledgements
We thank Allison Morningstar and Matthias Prestel for critically reading the manuscript. The authors are grateful to Mathias Jucker (Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany) for providing the APPPS1 mice and David Holtzman (Washington University School of Medicine, St Louis, Missouri, USA) for providing the ApoE antibody. We thank Haike Hampel for excellent technical assistance. Funds have been provided by the Alzheimer Forschung Initiative e.V. This work was also supported by the Deutsche Forschungsgemeinschaft (German Research Foundation) within the framework of the Munich Cluster for Systems Neurology (EXC 2145 SyNergy), the European Research Council (ERC-StG 802305) and the Vascular Dementia Research Foundation. C.H. is supported by a Koselleck Project of the DFG (HA1737/16-1) and the Helmholtz-Gemeinschaft (Zukunftsthema “Immunology and Inflammation” (ZT-0027)).