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

  1. Laura Sebastian Monasor
  2. Stephan A Müller
  3. Alessio Vittorio Colombo
  4. Gaye Tanrioever
  5. Jasmin König
  6. Stefan Roth
  7. Arthur Liesz
  8. Anna Berghofer
  9. Anke Piechotta
  10. Matthias Prestel
  11. Takashi Saito
  12. Takaomi C Saido
  13. Jochen Herms
  14. Michael Willem
  15. Christian Haass
  16. Stefan F Lichtenthaler  Is a corresponding author
  17. Sabina Tahirovic  Is a corresponding author
  1. German Center for Neurodegenerative Diseases (DZNE), Germany
  2. Graduate School of Systemic Neuroscience, Ludwig-Maximilians-University, Germany
  3. Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany
  4. Faculty of Chemistry, Technical University of Munich, Germany
  5. Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Germany
  6. Munich Cluster for Systems Neurology (SyNergy), Germany
  7. Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University, Germany
  8. Department of Molecular Drug Design and Target Validation, Fraunhofer Institute for Cell Therapy and Immunology, Germany
  9. Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science Institute, Japan
  10. Department of Neurocognitive Science, Nagoya City University Graduate School of Medical Science, Japan
  11. Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Germany
  12. Biomedical Center (BMC), Ludwig-Maximilians Universität München, Germany
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Cite this article as: eLife 2020;9:e54083 doi: 10.7554/eLife.54083

Abstract

Microglial dysfunction is a key pathological feature of Alzheimer's disease (AD), but little is known about proteome-wide changes in microglia during the course of AD 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 heterogeneity of microglial alterations during early, middle and advanced stages of Aβ deposition and 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 may trigger 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.

eLife digest

Alzheimer’s disease is a progressive, irreversible brain disorder. Patients with Alzheimer’s have problems with memory and other mental skills, which lead to more severe cognitive decline and, eventually, premature death. This is due to increasing numbers of nerve cells in the brain dying over time. A distinctive feature of Alzheimer’s is the abnormally high accumulation of a protein called amyloid-β, which forms distinctive clumps in the brain termed ‘plaques’.

The brain has a type of cells called the microglia that identify infections, toxic material and damaged cells, and prevent these from building up by clearing them away. In Alzheimer’s disease, however, the microglia do not work properly, which is thought to contribute to the accumulation of amyloid-β plaques. This means that people with mutations in the genes important for the microglia activity are also at higher risk of developing the disease.

Although problems with the microglia play an important role in Alzheimer’s, researchers still do not fully understand why microglia stop working in the first place. It is also not known exactly when and how the microglia change as Alzheimer’s disease progresses. To unravel this mystery, Sebastian Monasor, Müller et al. carried out a detailed study of the molecular ‘fingerprints’ of microglia at each key stage of Alzheimer’s disease.

The experiments used microglia cells from two different strains of genetically altered mice, both of which develop the hallmarks of Alzheimer’s disease, including amyloid-β plaques, at similar rates. Analysis of the proteins in microglia cells from both strains revealed distinctive, large-scale changes corresponding to successive stages of the disease – reflecting the gradual accumulation of plaques. Obvious defects in microglia function also appeared soon after plaques started to build up.

Microscopy imaging of the brain tissue showed that although amyloid-β plaques appeared at the same time, they looked different in each mouse strain. In one, plaques were more compact, while in the other, plaques appeared ‘fluffier’, like cotton wool. In mice with more compacted plaques, microglia recognized the plaques earlier and stopped working sooner, suggesting that plaque structure and microglia defects could be linked.

These results shed new light on the role of microglia and their changing protein ‘signals’ during the different stages of Alzheimer’s disease. In the future, this information could help identify people at risk for the disease, so that they can be treated as soon as possible, and to design new therapies to make microglia work again.

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 Αβ 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; Krabbe et al., 2013) and enhancing microglial phagocytic function has been explored as a therapeutic approach since substantial reduction of Aβ burden in mice appears to correlate with cognitive benefits (Bacskai et al., 2001; Bard et al., 2000; Bohrmann et al., 2012; Janus et al., 2000; Lathuilière et al., 2016; Morgan et al., 2000; Nicoll et al., 2006; Nicoll et al., 2003; Schenk et al., 1999; Schlepckow et al., 2020; 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 of microglia were previously revealed under physiological, neurodegenerative or neuroinflammatory conditions (Butovsky et al., 2014; Galatro et al., 2017; Gosselin et al., 2017; Götzl 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; Jordão et al., 2019; Keren-Shaul et al., 2017; Mathys et al., 2017; Sala Frigerio et al., 2019; Zhou et al., 2020). 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 (Böttcher 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 Microglial Aβ Response Proteins (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 may trigger 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 AD-associated proteomic signatures earlier compared to APP-KI microglia

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.

Figure 1 with 2 supplements see all
Quantitative proteomics of acutely isolated microglia.

Volcano plots of APPPS1 and APP-KI versus WT microglia at 1 (A and B), 3 (C and D), 6 (E and F) and 12 (G and H) months of age. The minus log10 transformed p-value is plotted against the log2 transformed LFQ ratios (log2 fold changes). A permutation-based FDR estimation was applied which is visualized as hyperbolic curves. Proteins with a log2 LFQ ratio lower than −0.5 or higher than +0.5 with a p-value less than 0.05 which remain significantly changed after FDR correction (FDR = 0.05, s0 = 0.1) are indicated as red circles. Non-significantly changed proteins are indicated as blue circles. Some selected proteins are marked with their gene names.

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 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).

Figure 2 with 2 supplements see all
APPPS1 microglia display earlier proteomic changes compared to APP-KI microglia.

(A) Profile plots of APPPS1 and APP-KI versus WT microglia at 1, 3, 6, and 12 months of age. Lines connect the average log2 fold changes of each protein at the different time points. Regulated proteins were grouped according to three profiles: significantly increased or decreased after FDR correction (log2 fold change >0.5 or<−0.5; p<0.05; FDR significant) at 3, 6, and 12 months or only at 6 and 12 or 12 months. Proteins that fulfill these criteria are indicated as red and blue lines for increased and decreased abundance, respectively. Selected up- or down-regulated proteins are indicated in orange and cyan. Heatmaps show the log2 fold changes of the top 74 up- or down-regulated proteins for early (B), middle (C) and advanced (D) MARPs and are compared to the log2 fold changes of transcripts from single cell transcriptome study (Keren-Shaul et al., 2017), indicated with K-S. Crosses indicate missing values.

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.

Figure 3 with 1 supplement see all
Gene ontology enrichment cluster analysis for biological process (BP) of MARPs.

Bar graphs show the clustering of early MARPs (A), middle MARPs (B) and advanced MARPs (C) for BP. Up- and down-regulated proteins were analyzed separately using the web-based software tool DAVID 6.8 with all consistently quantified proteins (5500) as an individual background. Significantly enriched clusters (Enrichment Score >1.301) for up-and down-regulated proteins are indicated in red and blue, respectively.

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.

Comparison of quantified and regulated proteins of APPPS1 and APP-KI mice at 12 months with single cell transcriptomics and biochemical validation of regulated proteins.

(A) The comparison of relatively quantified proteins of APPPS1 (yellow) and APP-KI (green) mice versus WT at 12 months shows a modest overlap with a single cell transcriptomics study (blue) (Keren-Shaul et al., 2017). An amount of 3348 proteins (57.2% of all quantified proteins) and their related transcripts were quantified in all three data sets. (B) The Venn diagram shows the overlap of significantly regulated proteins in APPPS1 (yellow) and APP-KI (green) mice versus WT at 12 months with unidirectionally regulated microglial transcripts (blue) (Keren-Shaul et al., 2017). Signatures which are specific either for the proteome or transcriptome dataset are separately depicted at the top or right, respectively. The indicated percentage values are based on all relatively quantified proteins. (C) Biochemical validation of proteomic data was performed using western blot analysis of microglial lysates from 12 month old mice. This analysis revealed an increased abundance of the up-regulated MARPs APOE, TREM2, CD68 and FABP5 as well as a decreased abundance of the down-regulated MARP CSF1R in APPPS1 and APP-KI compared to WT microglia. Arrows indicate antibody-detected specific bands and asterisk indicates unspecific bands.

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 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.

Figure 5 with 2 supplements see all
Microglial CLEC7a is prominently up-regulated in 3 month old APPPS1 mice.

Immunohistochemical analysis of CLEC7a (red) reveals an increased abundance of this early MARP in IBA1 positive (green) APPPS1 microglia surrounding Aβ plaques (magenta) that is less prominent in APP-KI microglia. CLEC7a is barely detected in WT microglia. Hoechst (HOE, blue) was used for nuclear staining. Boxed regions in upper panels (scale bar: 50 µm) are shown with a higher magnification in lower panels (scale bar: 10 µm).

Microglial CLEC7a is increased in both AD mouse models at 12 months of age.

Immunohistochemical analysis shows an up-regulation of CLEC7a (red) in IBA1 positive (green) APPPS1 and APP-KI microglia surrounding Aβ plaques (magenta), compared to WT where CLEC7a is barely detected. Hoechst (HOE, blue) was used for nuclear staining. Boxed regions in upper panels (scale bar: 50 µm) are shown with a higher magnification in lower panels (scale bar: 10 µm).

Microglial TMEM119 is down-regulated in both AD mouse models at 12 months of age.

Immunohistochemical analysis of TMEM119 (red) shows a broad coverage signal of this homeostatic marker in IBA1 positive (green) WT microglia. In the APPPS1 and APP-KI mice, TMEM119 signal is reduced in IBA1 positive microglia surrounding Aβ plaques (magenta). Of note, increased TMEM119 signal was often found in the core of amyloid plaques in APPPS1 and APP-KI mice. Hoechst (HOE, blue) was used for nuclear staining. Boxed regions in upper panels (scale bar: 50 µm) are shown with a higher magnification in lower panels (scale bar: 10 µm) and illustrate reduced levels of TMEM119 in plaque associated microglia.

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 (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.

Figure 8 with 1 supplement see all
APPPS1 Aβ plaques display a higher content of fibrillar Aβ compared to the APP-KI plaques.

(A) Immunohistochemical analysis showing total Aβ (magenta), fibrillar Αβ (ThR, red) and microglia (IBA1, green) in both mouse models at 3, 6 and 12 months of age. Scale bar: 100 µm B). Western blot analysis of insoluble Αβ at 3, 6 and 12 months of age confirms increased levels of fibrillar Αβ in APPPS1 compared to the APP-KI mice. (C). Quantification reveals increased total Αβ coverage in 3 month old APP-KI compared to the APPPS1 mice. Values are expressed as percentages of area covered by total Αβ and calculated as the mean (± SD) from N = 4 mice (****p<0.0001, unpaired two-tailed Student’s T-test).

Microglial recruitment correlates with fibrillar Aβ

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.

Figure 9 with 1 supplement see all
Fibrillar Aβ triggers higher microglial recruitment in 3 month old APPPS1 mice and correlates with a phagocytic impairment.

(A) IBA1 positive (green) microglia polarize towards the fibrillar Aβ core (ThR, red, white arrow), rather than to the surrounding plaque halo (magenta) in 3 month old APPPS1 and APP-KI mice. Hoechst (HOE, blue) was used for nuclear staining. Yellow arrows indicate intracellular fibrillar Aβ within APPPS1 microglia. Scale bar: 10 µm. (B) Quantification of IBA1 positive cells recruited to amyloid plaques in 3 month old APPPS1 and APP-KI mice. Microglial numbers are normalized to the plaque area. (C) Quantification of plaque size in 3 month old APPPS1 and APP-KI mice. (D) Immunohistochemical analysis of IBA1 (green) and CD68 (red) positive microglial cells recruited to Aβ plaques (magenta) in 3 month old APPPS1 and APP-KI mice. Hoechst (HOE, blue) was used for nuclear staining. Scale bar: 20 µm. (E) Quantification of CD68 coverage per single plaque in 3 month old APPPS1 and APP-KI mice. CD68 coverage is normalized to the plaque area. (F) Quantification of CD68 coverage per microglia in 3 month old APPPS1 and APP-KI mice. CD68 coverage is normalized to the number of IBA1 positive cells recruited to amyloid plaque. (G) Immunohistochemical analysis of microglia (IBA1, green) associated to dystrophic neurites (APP, red) in 3 month old APPPS1 and APP-KI mice. Aβ plaque was visualized using an antibody against Aβ (magenta) and Hoechst (HOE, blue) was used for nuclear staining. Scale bar: 10 µm. (H) Quantification of area covered by dystrophic neurites in 3 month old APPPS1 and APP-KI mice. (I) Quantification of IBA1 positive cells associated to dystrophic neurites in 3 month old APPPS1 and APP-KI mice. Number of microglia quantified per single plaque are normalized to its corresponding dystrophic neurite area. Values in (B, C), (H), and (I), are expressed as the mean of N = 5 (APPPS1) and N = 4 (APP-KI) mice ± SD and in (E and F) as the mean of N = 3 mice per genotype ± SD (*p<0.05; **p<0.01, unpaired two-tailed Student’s T-test; n.s.: not significant). (J). Mean Fluorescence Intensity (MFI) of E. coli-pHrodo signal within the CD11b positive cells in 3 and 6 month old APPPS1, APP-KI and WT mice. CytoD was used to inhibit phagocytosis and serves as a negative control. a.u.: arbitrary units. (K). Percentage of CD11b and E. coli-pHrodo double positive cells from the total CD11b positive population. In (J and K), values for the 3 month old group are expressed as the mean of N = 3 mice per genotype ± SD and for the 6 month old group as the mean of N = 2 mice per genotype ± SD (***p<0.001, Two-way ANOVA, Dunnett’s multiple comparison test; n.s.: not significant).

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.

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 (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.

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. We propose that the structure of amyloid plaques (fibrillar versus non-fibrillar) triggers 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 an average of 5699 (APPPS1) and 5698 (APP-KI) relatively quantified proteins. Notably, our analysis enhanced the detection of low abundance proteins and did not require data imputation. Although we included both male and female mice for the analysis of microglial proteome, our study was not powered to detect sex-specific differences that have been reported in microglia (Sala Frigerio et al., 2019). 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, PLP2 and MIF. 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, CTSD, CTSH, CTSZ, HEXA, GLB1, CD68, NPC2 and CLN3, 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 indicated 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). However, a recent study suggested little overlap between DAM and homeostatic profiles of rodent microglia and human AD patients, implicating a limitation of mouse models of AD (Zhou et al., 2020). Of note, microglial signatures have mostly been studied in amyloidosis mouse models and amyloid triggered microglial alterations cannot be directly compared with human microglia from advanced AD stages that are exposed to both amyloid and tau pathology. 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 may be triggered by fibrillar Aβ. This response could be mediated by dense core fibrillar amyloid and/or smaller fibrillar oligomers that have been proposed as neurotoxic species (Haass and Selkoe, 2007). Of note, microglial recruitment did not correlate with neuritic dystrophies as we detected prominent neuritic dystrophies in the APP-KI mice that bear less fibrillar Aβ. Similar plaque morphology with less fibrillar Aβ, as observed in APP-KI mice, has also been reported 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 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 cores (D'Andrea et al., 2004).

Although DAM signatures, that include upregulation of phagocytosis and lysosomal genes, have been suggested as a protective response (Keren-Shaul et al., 2017), there is still a lack of direct experimental evidence linking DAM profiles to improved microglial phagocytic function. In addition, it has been proposed that MGnD microglia may represent a dysfunctional phenotype (Krasemann et al., 2017). Importantly, our study demonstrates a functional link between proteomic changes and reduced phagocytosis by AD microglia. This is in agreement with Aβ−dependent early phagocytic dysfunction of APPPS1 microglia reported previously (Krabbe et al., 2013). Our study shows that 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. This functional link should be examined further using physiological substrates of microglia such as Aβ, myelin or synaptosomes (McQuade and Blurton-Jones, 2019).

Reduced phagocytosis of AD microglia might be related to observed proteomic alterations in lysosomal proteins or cell receptors. TREM2, which we found 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 phagocytic or lysosomal proteins (e.g., cathepsins or CD68) that are part of MARPs and altered in APPPS1 and APP-KI microglia. Similarly, also transcriptional analysis revealed a downregulation of homeostatic and upregulation of DAM or MGnD program within microglial cells in the vicinity of Aβ plaques (Keren-Shaul et al., 2017; Krasemann et al., 2017). The increase in lysosomal or phagocytic gene signatures may reflect a compensatory mechanism initiated as a response of microglia to Aβ accumulation in order to enhance phagocytic function. Eventually, this limited 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 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. These proteomic changes in microglia seem to 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

Key resources table
Reagent type
(species) or
resource
DesignationSource or referenceIdentifiersAdditional
information
Genetic reagent (Mus musculus)APPPS1doi: 10.1038/sj.embor.7400784RRID:MGI:5313568Heterozygous (C57BL/6 background)
Genetic reagent (Mus musculus)APPNL-G-F (APP-KI)doi: 10.1038/nn.3697RRID:MGI:6160916Homozygous (C57BL/6 background)
AntibodyAnti-IBA1 (rabbit polyclonal)WakoCat#:019–19741, RRID:AB_839504IHC (1:500)
AntibodyAnti-IBA1(goat polyclonal)AbcamCat#:ab5076, RRID:AB_2224402IHC (1:500)
AntibodyAnti-Amyloid beta NAB228 (mouse monoclonal)Santa CruzCat#:sc-32277, RRID:AB_626670IHC (1:2000)
AntibodyAnti-CD68 (rat monoclonal)Bio-RadCat#:MCA1957G, RRID:AB_324217IHC (1:500) WB (1:1000)
AntibodyAnti-TREM2 (sheep polyclonal)R and D SystemsCat#:AF1729, RRID:AB_354956IHC (1:50)
AntibodyAnti-Amyloid Y188 (rabbit monoclonal)AbcamCat#:1565–1, RRID:AB_562042IHC (1:2000)
AntibodyAnti-CLEC7a (rat monoclonal)InvivogenCat#:mabg-mdect, RRID:AB_2753143IHC (1:50)
AntibodyAnti-TMEM119 (rabbit monoclonal)AbcamCat#:ab209064, RRID:AB_2800343IHC (1:200)
AntibodyAnti-APOE HJ6.3 (mouse monoclonal)doi: 10.1084/jem.20121274IHC (Biotinylated, 1:100)
AntibodyAnti-Amyloid beta 3552 (rabbit polyclonal)doi:10.1523/JNEUROSCI.05.2006IHC (1:5000), WB (1:2000)
AntibodyAnti-pE3-Amyloid beta J8 (mouse monoclonal)doi:10.3390/molecules23040924IHC (1:500)
AntibodyAnti-TREM2 5F4 (rat monoclonal)doi: 10.15252/emmm.201606370WB (1:10)
AntibodyAnti-APOE (goat polyclonal)MilliporeCat#:AB947, RRID:AB_2258475WB (1:1000)
AntibodyAnti-CSF1R (rabbit polyclonal)Cell SignalingCat#:3152, RRID:AB_2085233WB (1:1000)
AntibodyAnti-FABP5 (goat polyclonal)R and DSystemsCat#:AF1476, RRID:AB_2293656WB (1:400)
AntibodyAnti-GAPDH (mouse monoclonal)AbcamCat#:ab8245, RRID:AB_2107448WB (1:2000)
AntibodyAnti-CD11b-APC-Cy7 (rat monoclonal)BDBiosciencesCat#:557657, RRID:AB_396772FACS (1:200)

Animals

Male and female mice of the hemizygous APPPS1 mouse line overexpressing human APPKM670/671NL and PS1L166P under the control of the Thy1 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 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.

Isolation of primary microglia

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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.

Sample preparation for mass spectrometry

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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).

Liquid chromatography – tandem mass spectrometry analysis

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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; Supplementary file 4). 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

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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 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.

Biochemical characterization of brain tissue and isolated microglia

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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.

Immunohistochemistry

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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.

Image acquisition, analysis and quantifications

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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 Supplementary file 6A and B, respectively.

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).

LCO staining and spectral analysis

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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 of E. coli particles

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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.

FACS analysis

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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 data analysis

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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).

Data availability

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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.

References

  1. 1
  2. 2
  3. 3
  4. 4
  5. 5
  6. 6
  7. 7
  8. 8
  9. 9
  10. 10
  11. 11
  12. 12
  13. 13
  14. 14
  15. 15
  16. 16
  17. 17
  18. 18
  19. 19
  20. 20
  21. 21
  22. 22
  23. 23
  24. 24
  25. 25
  26. 26
  27. 27
  28. 28
  29. 29
  30. 30
  31. 31
  32. 32
  33. 33
  34. 34
  35. 35
  36. 36
  37. 37
  38. 38
  39. 39
  40. 40
  41. 41
  42. 42
  43. 43
  44. 44
  45. 45
  46. 46
  47. 47
  48. 48
  49. 49
  50. 50
  51. 51
  52. 52
  53. 53
  54. 54
  55. 55
  56. 56
  57. 57
  58. 58
  59. 59
  60. 60
  61. 61
  62. 62
  63. 63
  64. 64
  65. 65
    Common variants at MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 are associated with late-onset Alzheimer's disease
    1. AC Naj
    2. G Jun
    3. GW Beecham
    4. LS Wang
    5. BN Vardarajan
    6. J Buros
    7. PJ Gallins
    8. JD Buxbaum
    9. GP Jarvik
    10. PK Crane
    11. EB Larson
    12. TD Bird
    13. BF Boeve
    14. NR Graff-Radford
    15. PL De Jager
    16. D Evans
    17. JA Schneider
    18. MM Carrasquillo
    19. N Ertekin-Taner
    20. SG Younkin
    21. C Cruchaga
    22. JS Kauwe
    23. P Nowotny
    24. P Kramer
    25. J Hardy
    26. MJ Huentelman
    27. AJ Myers
    28. MM Barmada
    29. FY Demirci
    30. CT Baldwin
    31. RC Green
    32. E Rogaeva
    33. P St George-Hyslop
    34. SE Arnold
    35. R Barber
    36. T Beach
    37. EH Bigio
    38. JD Bowen
    39. A Boxer
    40. JR Burke
    41. NJ Cairns
    42. CS Carlson
    43. RM Carney
    44. SL Carroll
    45. HC Chui
    46. DG Clark
    47. J Corneveaux
    48. CW Cotman
    49. JL Cummings
    50. C DeCarli
    51. ST DeKosky
    52. R Diaz-Arrastia
    53. M Dick
    54. DW Dickson
    55. WG Ellis
    56. KM Faber
    57. KB Fallon
    58. MR Farlow
    59. S Ferris
    60. MP Frosch
    61. DR Galasko
    62. M Ganguli
    63. M Gearing
    64. DH Geschwind
    65. B Ghetti
    66. JR Gilbert
    67. S Gilman
    68. B Giordani
    69. JD Glass
    70. JH Growdon
    71. RL Hamilton
    72. LE Harrell
    73. E Head
    74. LS Honig
    75. CM Hulette
    76. BT Hyman
    77. GA Jicha
    78. LW Jin
    79. N Johnson
    80. J Karlawish
    81. A Karydas
    82. JA Kaye
    83. R Kim
    84. EH Koo
    85. NW Kowall
    86. JJ Lah
    87. AI Levey
    88. AP Lieberman
    89. OL Lopez
    90. WJ Mack
    91. DC Marson
    92. F Martiniuk
    93. DC Mash
    94. E Masliah
    95. WC McCormick
    96. SM McCurry
    97. AN McDavid
    98. AC McKee
    99. M Mesulam
    100. BL Miller
    101. CA Miller
    102. JW Miller
    103. JE Parisi
    104. DP Perl
    105. E Peskind
    106. RC Petersen
    107. WW Poon
    108. JF Quinn
    109. RA Rajbhandary
    110. M Raskind
    111. B Reisberg
    112. JM Ringman
    113. ED Roberson
    114. RN Rosenberg
    115. M Sano
    116. LS Schneider
    117. W Seeley
    118. ML Shelanski
    119. MA Slifer
    120. CD Smith
    121. JA Sonnen
    122. S Spina
    123. RA Stern
    124. RE Tanzi
    125. JQ Trojanowski
    126. JC Troncoso
    127. VM Van Deerlin
    128. HV Vinters
    129. JP Vonsattel
    130. S Weintraub
    131. KA Welsh-Bohmer
    132. J Williamson
    133. RL Woltjer
    134. LB Cantwell
    135. BA Dombroski
    136. D Beekly
    137. KL Lunetta
    138. ER Martin
    139. MI Kamboh
    140. AJ Saykin
    141. EM Reiman
    142. DA Bennett
    143. JC Morris
    144. TJ Montine
    145. AM Goate
    146. D Blacker
    147. DW Tsuang
    148. H Hakonarson
    149. WA Kukull
    150. TM Foroud
    151. JL Haines
    152. R Mayeux
    153. MA Pericak-Vance
    154. LA Farrer
    155. GD Schellenberg
    (2011)
    Nature Genetics 43:436–441.
    https://doi.org/10.1038/ng.801
  66. 66
  67. 67
  68. 68
  69. 69
  70. 70
  71. 71
  72. 72
  73. 73
  74. 74
  75. 75
  76. 76
  77. 77
  78. 78
  79. 79
  80. 80
  81. 81
  82. 82
  83. 83
  84. 84
  85. 85
  86. 86
  87. 87
  88. 88
    Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer's disease
    1. R Sims
    2. SJ van der Lee
    3. AC Naj
    4. C Bellenguez
    5. N Badarinarayan
    6. J Jakobsdottir
    7. BW Kunkle
    8. A Boland
    9. R Raybould
    10. JC Bis
    11. ER Martin
    12. B Grenier-Boley
    13. S Heilmann-Heimbach
    14. V Chouraki
    15. AB Kuzma
    16. K Sleegers
    17. M Vronskaya
    18. A Ruiz
    19. RR Graham
    20. R Olaso
    21. P Hoffmann
    22. ML Grove
    23. BN Vardarajan
    24. M Hiltunen
    25. MM Nöthen
    26. CC White
    27. KL Hamilton-Nelson
    28. J Epelbaum
    29. W Maier
    30. SH Choi
    31. GW Beecham
    32. C Dulary
    33. S Herms
    34. AV Smith
    35. CC Funk
    36. C Derbois
    37. AJ Forstner
    38. S Ahmad
    39. H Li
    40. D Bacq
    41. D Harold
    42. CL Satizabal
    43. O Valladares
    44. A Squassina
    45. R Thomas
    46. JA Brody
    47. L Qu
    48. P Sánchez-Juan
    49. T Morgan
    50. FJ Wolters
    51. Y Zhao
    52. FS Garcia
    53. N Denning
    54. M Fornage
    55. J Malamon
    56. MCD Naranjo
    57. E Majounie
    58. TH Mosley
    59. B Dombroski
    60. D Wallon
    61. MK Lupton
    62. J Dupuis
    63. P Whitehead
    64. L Fratiglioni
    65. C Medway
    66. X Jian
    67. S Mukherjee
    68. L Keller
    69. K Brown
    70. H Lin
    71. LB Cantwell
    72. F Panza
    73. B McGuinness
    74. S Moreno-Grau
    75. JD Burgess
    76. V Solfrizzi
    77. P Proitsi
    78. HH Adams
    79. M Allen
    80. D Seripa
    81. P Pastor
    82. LA Cupples
    83. ND Price
    84. D Hannequin
    85. A Frank-García
    86. D Levy
    87. P Chakrabarty
    88. P Caffarra
    89. I Giegling
    90. AS Beiser
    91. V Giedraitis
    92. H Hampel
    93. ME Garcia
    94. X Wang
    95. L Lannfelt
    96. P Mecocci
    97. G Eiriksdottir
    98. PK Crane
    99. F Pasquier
    100. V Boccardi
    101. I Henández
    102. RC Barber
    103. M Scherer
    104. L Tarraga
    105. PM Adams
    106. M Leber
    107. Y Chen
    108. MS Albert
    109. S Riedel-Heller
    110. V Emilsson
    111. D Beekly
    112. A Braae
    113. R Schmidt
    114. D Blacker
    115. C Masullo
    116. H Schmidt
    117. RS Doody
    118. G Spalletta
    119. WT Longstreth
    120. TJ Fairchild
    121. P Bossù
    122. OL Lopez
    123. MP Frosch
    124. E Sacchinelli
    125. B Ghetti
    126. Q Yang
    127. RM Huebinger
    128. F Jessen
    129. S Li
    130. MI Kamboh
    131. J Morris
    132. O Sotolongo-Grau
    133. MJ Katz
    134. C Corcoran
    135. M Dunstan
    136. A Braddel
    137. C Thomas
    138. A Meggy
    139. R Marshall
    140. A Gerrish
    141. J Chapman
    142. M Aguilar
    143. S Taylor
    144. M Hill
    145. MD Fairén
    146. A Hodges
    147. B Vellas
    148. H Soininen
    149. I Kloszewska
    150. M Daniilidou
    151. J Uphill
    152. Y Patel
    153. JT Hughes
    154. J Lord
    155. J Turton
    156. AM Hartmann
    157. R Cecchetti
    158. C Fenoglio
    159. M Serpente
    160. M Arcaro
    161. C Caltagirone
    162. MD Orfei
    163. A Ciaramella
    164. S Pichler
    165. M Mayhaus
    166. W Gu
    167. A Lleó
    168. J Fortea
    169. R Blesa
    170. IS Barber
    171. K Brookes
    172. C Cupidi
    173. RG Maletta
    174. D Carrell
    175. S Sorbi
    176. S Moebus
    177. M Urbano
    178. A Pilotto
    179. J Kornhuber
    180. P Bosco
    181. S Todd
    182. D Craig
    183. J Johnston
    184. M Gill
    185. B Lawlor
    186. A Lynch
    187. NC Fox
    188. J Hardy
    189. RL Albin
    190. LG Apostolova
    191. SE Arnold
    192. S Asthana
    193. CS Atwood
    194. CT Baldwin
    195. LL Barnes
    196. S Barral
    197. TG Beach
    198. JT Becker
    199. EH Bigio
    200. TD Bird
    201. BF Boeve
    202. JD Bowen
    203. A Boxer
    204. JR Burke
    205. JM Burns
    206. JD Buxbaum
    207. NJ Cairns
    208. C Cao
    209. CS Carlson
    210. CM Carlsson
    211. RM Carney
    212. MM Carrasquillo
    213. SL Carroll
    214. CC Diaz
    215. HC Chui
    216. DG Clark
    217. DH Cribbs
    218. EA Crocco
    219. C DeCarli
    220. M Dick
    221. R Duara
    222. DA Evans
    223. KM Faber
    224. KB Fallon
    225. DW Fardo
    226. MR Farlow
    227. S Ferris
    228. TM Foroud
    229. DR Galasko
    230. M Gearing
    231. DH Geschwind
    232. JR Gilbert
    233. NR Graff-Radford
    234. RC Green
    235. JH Growdon
    236. RL Hamilton
    237. LE Harrell
    238. LS Honig
    239. MJ Huentelman
    240. CM Hulette
    241. BT Hyman
    242. GP Jarvik
    243. E Abner
    244. LW Jin
    245. G Jun
    246. A Karydas
    247. JA Kaye
    248. R Kim
    249. NW Kowall
    250. JH Kramer
    251. FM LaFerla
    252. JJ Lah
    253. JB Leverenz
    254. AI Levey
    255. G Li
    256. AP Lieberman
    257. KL Lunetta
    258. CG Lyketsos
    259. DC Marson
    260. F Martiniuk
    261. DC Mash
    262. E Masliah
    263. WC McCormick
    264. SM McCurry
    265. AN McDavid
    266. AC McKee
    267. M Mesulam
    268. BL Miller
    269. CA Miller
    270. JW Miller
    271. JC Morris
    272. JR Murrell
    273. AJ Myers
    274. S O'Bryant
    275. JM Olichney
    276. VS Pankratz
    277. JE Parisi
    278. HL Paulson
    279. W Perry
    280. E Peskind
    281. A Pierce
    282. WW Poon
    283. H Potter
    284. JF Quinn
    285. A Raj
    286. M Raskind
    287. B Reisberg
    288. C Reitz
    289. JM Ringman
    290. ED Roberson
    291. E Rogaeva
    292. HJ Rosen
    293. RN Rosenberg
    294. MA Sager
    295. AJ Saykin
    296. JA Schneider
    297. LS Schneider
    298. WW Seeley
    299. AG Smith
    300. JA Sonnen
    301. S Spina
    302. RA Stern
    303. RH Swerdlow
    304. RE Tanzi
    305. TA Thornton-Wells
    306. JQ Trojanowski
    307. JC Troncoso
    308. VM Van Deerlin
    309. LJ Van Eldik
    310. HV Vinters
    311. JP Vonsattel
    312. S Weintraub
    313. KA Welsh-Bohmer
    314. KC Wilhelmsen
    315. J Williamson
    316. TS Wingo
    317. RL Woltjer
    318. CB Wright
    319. CE Yu
    320. L Yu
    321. F Garzia
    322. F Golamaully
    323. G Septier
    324. S Engelborghs
    325. R Vandenberghe
    326. PP De Deyn
    327. CM Fernadez
    328. YA Benito
    329. H Thonberg
    330. C Forsell
    331. L Lilius
    332. A Kinhult-Stählbom
    333. L Kilander
    334. R Brundin
    335. L Concari
    336. S Helisalmi
    337. AM Koivisto
    338. A Haapasalo
    339. V Dermecourt
    340. N Fievet
    341. O Hanon
    342. C Dufouil
    343. A Brice
    344. K Ritchie
    345. B Dubois
    346. JJ Himali
    347. CD Keene
    348. J Tschanz
    349. AL Fitzpatrick
    350. WA Kukull
    351. M Norton
    352. T Aspelund
    353. EB Larson
    354. R Munger
    355. JI Rotter
    356. RB Lipton
    357. MJ Bullido
    358. A Hofman
    359. TJ Montine
    360. E Coto
    361. E Boerwinkle
    362. RC Petersen
    363. V Alvarez
    364. F Rivadeneira
    365. EM Reiman
    366. M Gallo
    367. CJ O'Donnell
    368. JS Reisch
    369. AC Bruni
    370. DR Royall
    371. M Dichgans
    372. M Sano
    373. D Galimberti
    374. P St George-Hyslop
    375. E Scarpini
    376. DW Tsuang
    377. M Mancuso
    378. U Bonuccelli
    379. AR Winslow
    380. A Daniele
    381. CK Wu
    382. O Peters
    383. B Nacmias
    384. M Riemenschneider
    385. R Heun
    386. C Brayne
    387. DC Rubinsztein
    388. J Bras
    389. R Guerreiro
    390. A Al-Chalabi
    391. CE Shaw
    392. J Collinge
    393. D Mann
    394. M Tsolaki
    395. J Clarimón
    396. R Sussams
    397. S Lovestone
    398. MC O'Donovan
    399. MJ Owen
    400. TW Behrens
    401. S Mead
    402. AM Goate
    403. AG Uitterlinden
    404. C Holmes
    405. C Cruchaga
    406. M Ingelsson
    407. DA Bennett
    408. J Powell
    409. TE Golde
    410. C Graff
    411. PL De Jager
    412. K Morgan
    413. N Ertekin-Taner
    414. O Combarros
    415. BM Psaty
    416. P Passmore
    417. SG Younkin
    418. C Berr
    419. V Gudnason
    420. D Rujescu
    421. DW Dickson
    422. JF Dartigues
    423. AL DeStefano
    424. S Ortega-Cubero
    425. H Hakonarson
    426. D Campion
    427. M Boada
    428. JK Kauwe
    429. LA Farrer
    430. C Van Broeckhoven
    431. MA Ikram
    432. L Jones
    433. JL Haines
    434. C Tzourio
    435. LJ Launer
    436. V Escott-Price
    437. R Mayeux
    438. JF Deleuze
    439. N Amin
    440. PA Holmans
    441. MA Pericak-Vance
    442. P Amouyel
    443. CM van Duijn
    444. A Ramirez
    445. LS Wang
    446. JC Lambert
    447. S Seshadri
    448. J Williams
    449. GD Schellenberg
    450. ARUK Consortium, GERAD/PERADES, CHARGE, ADGC, EADI
    (2017)
    Nature Genetics 49:1373–1384.
    https://doi.org/10.1038/ng.3916
  89. 89
  90. 90
  91. 91
  92. 92
  93. 93
  94. 94
  95. 95
  96. 96
  97. 97
  98. 98
  99. 99
  100. 100
  101. 101
  102. 102
  103. 103
  104. 104
  105. 105

Decision letter

  1. Beth Stevens
    Reviewing Editor; Boston Children's Hospital, United States
  2. Huda Y Zoghbi
    Senior Editor; Texas Children's Hospital, United States
  3. Beth Stevens
    Reviewer; Boston Children's Hospital, United States
  4. Mathew Blurton-Jones
    Reviewer
  5. Tara L Spires-Jones
    Reviewer; University of Edinburgh, United Kingdom

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

Monasor and colleagues perform a detailed and systematic analysis of age-related changes in the microglial proteome across two independent mouse models of Alzheimer's disease and show that changes in the microglial proteome are more closely associated with increasing fibrillar amyloid and relatively independent of dystrophic neurite pathology. Their findings suggest plaque-associated microglia exhibit impaired phagocytosis that begins relatively early in the disease process. Interestingly, they find that proteomic changes correlate only partially with prior RNA-seq analyses of AD mice, revealing many additional changes that were not previously detected at the mRNA level, further demonstrating the importance of examining the microglial proteome. Overall this work provides new insight into the microglial responses to plaque pathology and will be a useful resource for the field.

Decision letter after peer review:

Thank you for submitting your article "Fibrillar Aβ triggers microglial proteome alterations and dysfunction in Alzheimer mouse models" for consideration by eLife. Your article has been reviewed by three peer reviewers, including Beth Stevens as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen by Huda Zoghbi as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity: Mathew Blurton-Jones (Reviewer #2); Tara L Spires-Jones (Reviewer #3).

The reviewers have discussed the reviews with one another, and the Reviewing Editor has drafted this decision to help you prepare a revised submission.

Sebastian Monasor and colleagues report a study examining proteomic changes in microglia in 2 mouse models of Alzheimer's disease, APPPS1 and APP-NL-G-F mice. This work builds upon a solid number of single cell transcriptomic papers showing disease associated changes in microglia in plaque bearing models and human AD brain. Here, the multiple time points and use of proteomics expands what we know about microglial changes in AD models. Authors isolated microglia from mouse brains, optimized a proteomic workflow, then used data independent acquisition for label free quantification of proteins. They observed protein signatures similar to wild-type mice at 1 month of age in both lines. In APPPS1 mice, early microglial protein changes in were observed at 3 months of age with further changes at 6 and 12 months. APP-NL-G-F mice exhibited changes later, starting at 6 months of age.

All reviewers agree that this paper has the potential to be a useful resource given our limited knowledge about the microglia proteome in aging and disease models. There is a nice use of two models that show consistent findings. These datasets contain further confirmation that some of the AD risk genes are acting through changes in protein levels in microglia.

The proteomics datasets could be a valuable resource for the field; however, there are significant concerns about robustness of data and the lack of transparency and details on methods, statistics and analyses that must be addressed as detailed by reviewer 3.

Essential revisions:

1) Provide more information and clarity on methods, experimental design, sample size.

In addition, there were shared concerns raised about rigor and lack of details on statistical analyses and specific tests used on all figures. Please see specific concerns detailed by reviewer 3, each one needs to be addressed.

2) Address possible sex-specific effects and report the number of makes and female mice used in each experiment. Were groups balanced to include equivalent numbers of male and female mice? Were the experiments powered to include a sex-dependent analysis?

3) Is there indeed an inverse correlation between the number of plaque-associated microglia and dystrophic neurite area? Please include a regression analysis of these data across the two mouse lines.

4) Please move these two Venn diagrams from Supplemental figures 2 and 4C into the main figures.

5) A more in-depth characterization of the different amyloid types between the Transgenic and knock in models. The authors highlight differences in fibrillar amyloid and plaque compaction between the models and that proteomic changes more closely correlated with the fibrillar Aβ than the neuritic dystrophy However, would be helpful to see more quantification and examination of oligomeric or N-terminal (pyroglu-Aβ) modified species and to address whether the changes also followed differences in those species.

Reviewer #3:

Strengths:

Overall this is a clearly written paper on an important topic. There is a very nice use of 2 models, and I appreciate that the authors examined changes that were consistent between the 2 lines. These datasets contain nice further confirmation that some of the AD risk genes are acting through changes in protein levels in microglia. The shared proteomics datasets will also be very useful for the field.

Critiques:

I have a substantive concern with the robustness of the data. This may be completely alleviated by more methods details, or there may be some experimental design and analysis problems that need addressing. More details are needed to understand better. Namely:

How many mice were used per group in the proteomic study? Were all microglia from all of the mice in a single group pooled or were they analysed separately then an average taken per group?

I'm concerned by the use of Student’s t-tests in proteomic abundance comparisons as there were multiple testing and multiple genotypes. Is this robust?

What were the sexes of the mice, were they sex balanced, and were there sex effects? It is known that there are sex differences in some microglial phenotypes.

Similarly, in the histology studies, 3 mice per group is not very robust and it looks like the data analysis may be pseudoreplicated. In the Materials and methods, it states that 3 mice were used per group with 6 sections from each mouse and 5 plaques for each section, so 30 images per mouse. Were the images all from neocortex? Were the 6 sections per mouse systematically chosen through the brain and from the same locations in each mouse?

Importantly, in the analysis of Figure 8, it is not clear whether the experimental unit is the individual mouse or individual plaque, e.g. is the n 3 or 90? If the latter, the data are pseudoreplicated and not robust.

What were the sex of the mice in IHC studies and were there sex effects?

https://doi.org/10.7554/eLife.54083.sa1

Author response

Essential revisions:

1) Provide more information and clarity on methods, experimental design, sample size.

In addition, there were shared concerns raised about rigor and lack of details on statistical analyses and specific tests used on all figures. Please see specific concerns detailed by reviewer 3, each one needs to be addressed.

Concerns detailed by reviewer #3:

1.1) How many mice were used per group in the proteomic study? Were all microglia from all of the mice in a single group pooled or were they analysed separately then an average taken per group?

For our proteomic analysis, we used 3 animals per age group and genotype. The extracted microglial lysates were measured and analysed for each animal individually (“Microglia were isolated from 3 transgenic animals and their 3 corresponding WT controls for each age and genotype…Microglia-enriched pellets from individual animals were analysed separately…”). Statistical testing was based on label-free quantification (LFQ) intensities of the 3 individual animals per group and outlined as follows: “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”.

1.2) I'm concerned by the use of Student’s t-tests in proteomic abundance comparisons as there were multiple testing and multiple genotypes. Is this robust?

As mass spectrometry analysis of the two transgenic lines was performed in separate batches, LFQ intensities can only be compared between the transgenic and WT mice of the same age. Since we did not perform multiple comparisons, the t-test is the appropriate choice for statistical analysis. Taking into consideration that our sample size is small (N=3), we applied the rather conservative FDR correction (5%) that reduces false positive findings and controls for the possible inflation of the p-value. Furthermore, we have defined minimal threshold of log2 fold change of 0.5 (≙ 1.41-fold on linear scale) to avoid minor, but statistically significant changes.

1.3) What were the sexes of the mice, were they sex balanced, and were there sex effects? It is known that there are sex differences in some microglial phenotypes.

Similarly, in the histology studies, 3 mice per group is not very robust and it looks like the data analysis may be pseudoreplicated. In the Materials and methods, it states that 3 mice were used per group with 6 sections from each mouse and 5 plaques for each section, so 30 images per mouse. Were the images all from neocortex? Were the 6 sections per mouse systematically chosen through the brain and from the same locations in each mouse?

We have included both male and female mice into our analysis. Although we acknowledge sex-specific effects on microglial phenotypes, our experiments are unfortunately not powered to enable sex-dependent analysis. To counteract possible sex-dependent effects, our experimental groups for the proteomic analysis were sex-balanced (with the exception of APPPS1 at 12 months). We have outlined the sex of mice analyzed in all experiments in Supplementary files 3 (proteomics), 5 (IHC) and 7 (FACS) and discussed this limitation of our study as follows: “Although, we included both male and female mice for the analysis of microglial proteome, our study was not powered to detect sex-specific differences that have been reported in microglia (Sala Frigerio et al., 2019)”. For our histological studies, we increased the number of analysed animals (from N=3 to N=4 for APP-KI and N=5 for APPPS1) in our revised manuscript for experiments delineated in Figures 9B, C, H and I. The 6 sections that were analysed per mouse in our IHC studies of amyloid plaques were systematically chosen coronal sections that represent comparable brain regions in each mouse. 30 randomly selected plaques from the neocortex were then analyzed per mouse (subsection “Image acquisition, analysis and quantifications” and Supplementary file 5).

1.4) Importantly, in the analysis of Figure 8, it is not clear whether the experimental unit is the individual mouse or individual plaque, eg is the n 3 or 90? If the latter, the data are pseudoreplicated and not robust.

The experimental unit used for statistical testing in Figure 9 (former Figure 8) is the individual mouse (N=3 or higher in the revised manuscript) and not the individual plaque. This information is outlined in Supplementary file 5 of our revised manuscript.

1.5) What were the sex of the mice in IHC studies and were there sex effects?

As stated above, the sex of all mice analyzed by IHC are outlined in Supplementary file 5. Unfortunately, our experiments did not allow a sex-dependent analysis (Discussion paragraph two).

2) Address possible sex-specific effects and report the number of makes and female mice used in each experiment. Were groups balanced to include equivalent numbers of male and female mice? Were the experiments powered to include a sex-dependent analysis?

As outlined above, our experiments are unfortunately not powered to include a sex-dependent analysis. We acknowledge sex-specific effects on microglia and thus our experimental groups for the proteomic analysis (with the exception of APPPS1 at 12 months) were sex-balanced. We reported numbers of male and female mice analysed in all experiments in Supplementary files 3 (proteomics), 5 (IHC) and 7 (FACS) and discussed this limitation of our study as follows: “Although, we included both male and female mice for the analysis of microglial proteome, our study was not powered to detect sex-specific differences that have been reported in microglia (Sala Frigerio et al., 2019)”.

3) Is there indeed an inverse correlation between the number of plaque-associated microglia and dystrophic neurite area? Please include a regression analysis of these data across the two mouse lines.

As pointed out by the reviewer, we found an increased dystrophic neurite area in the APP-KI compared to the APPPS1 mice and reduced number of microglial cells recruited to this area. This additional quantification analysis is now depicted in new Figure 9I. As suggested by the reviewer, we also performed a regression analysis between the number of plaque-associated microglia and dystrophic neurite area, but could not find an inverse correlation (Author response image 1). Therefore, our data do not provide evidence that more microglia around amyloid plaques are directly responsible for less neuritic damage.

Author response image 1
Low correlation between dystrophic neurite size and microglia recruitment.

Correlation and linear regression analysis of dystrophic neurite size and microglia recruitment per plaque in APPPS1 (A) and APP-KI (B) mice. rs indicates the Spearman’s rank correlation coefficient, p<0.05: significant, p≥ 0.05: non significant. Different colors represent biological replicates (APPPS1: N=5 mice, APP-KI: N=4 mice) and the same color is used to show technical replicates.

4) Please move these two Venn diagrams from supplemental figures 2 and 4C into the main figures.

We appreciate this comment and followed the reviewer’s recommendation. A new Figure 4 is now depicting a comparison between our two models (12 months) and Keren-Shaul dataset, in both numbers of identified proteins (Figure 4A) and numbers of unidirectionally regulated proteins (Figure 4B) as described in paragraph seven of subsection “Identification of MARPs as signatures of early, middle and advanced amyloid stages”. Of note, as former supplemental Figure 2D (an overlap of relatively quantified proteins in APPPS1 and APP-KI at 12 months) supports that our proteomic workflow in both models is highly reproducible, we still left this technical information in Figure 1—figure supplement 1D.

5) A more in-depth characterization of the different amyloid types between the Transgenic and knock in models. The authors highlight differences in fibrillar amyloid and plaque compaction between the models and that proteomic changes more closely correlated with the fibrillar Aβ than the neuritic dystrophy However, would be helpful to see more quantification and examination of oligomeric or N-terminal (pyroglu-Aβ) modified species and to address whether the changes also followed differences in those species.

In the revised manuscript we quantified the coverage of total Aβ at 3 months of age in both mouse models (Figure 8C), and found an increased Aβ coverage in the APP-KI model (subsection “APPPS1 and APP-KI mice show similar dynamics of amyloid plaque deposition, but differ in plaque fibrillization”), supporting that Aβ conformation/fibrillarity, rather than plaque size, are responsible for microglial changes. To better characterize different amyloid types in APPPS1 and APP-KI mouse models, we have performed spectral analysis using the two luminescent conjugated oligothiophenes (LCOs), qFTAA and hFTAA that preferably bind to dense core/fibrillar or pre-fibrillar Aβ, respectively. These new data are included into Figure 8—figure supplement 1A-D and reveal that dense core fibrillar Aβ could only be detected in the APPPS1 model at the age of 3 months. In contrast, fibrillar Aβ could be detected in both models at the age of 12 months, corresponding to the similar proteomic signatures (MARPs) and supporting the hypothesis that fibrillar Aβ may be triggering proteomic changes. Nevertheless, our data cannot exclude the effect of dystrophic neurites onto microglial populations and we therefore toned down our statement “Microglial recruitment is triggered by fibrillar Aβ and not by dystrophic neurites” into “Microglial recruitment correlates with fibrillar Aβ”.

Furthermore, according to reviewers’ recommendations, we have expanded our study and examined a correlation between microglial activation and pyroglu-Aβ species (pE3-Aβ) that are included into the new Figure 9—figure supplement 1A-D (subsection “Microglial recruitment correlates with by fibrillar Aβ”). We detected and quantified pE3-Aβreactivity in 12 month old APPPS1 and APP-KI animals (Figure 9—figure supplement 1B-D). However, we could not detect pyroglu-Aβ signal in 3 month old animals (Figure 9—figure supplement 1A), suggesting that it is less likely that this modification is responsible for microglial recruitment and differences in the proteome between the models.

https://doi.org/10.7554/eLife.54083.sa2

Article and author information

Author details

  1. Laura Sebastian Monasor

    1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    2. Graduate School of Systemic Neuroscience, Ludwig-Maximilians-University, Munich, Germany
    Contribution
    Formal analysis, Validation, Investigation, Visualization, Methodology, Writing - original draft
    Contributed equally with
    Stephan A Müller
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7864-7400
  2. Stephan A Müller

    German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    Contribution
    Resources, Software, Formal analysis, Investigation, Visualization, Methodology, Writing - original draft
    Contributed equally with
    Laura Sebastian Monasor
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3414-307X
  3. Alessio Vittorio Colombo

    German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    Contribution
    Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Gaye Tanrioever

    1. German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany
    2. Department of Cellular Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
    Contribution
    Formal analysis, Investigation, Methodology
    Competing interests
    No competing interests declared
  5. Jasmin König

    1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    2. Faculty of Chemistry, Technical University of Munich, Garching, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  6. Stefan Roth

    Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Munich, Germany
    Contribution
    Formal analysis, Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  7. Arthur Liesz

    1. Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Munich, Germany
    2. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
    Contribution
    Supervision, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  8. Anna Berghofer

    Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University, Munich, Germany
    Contribution
    Investigation, Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  9. Anke Piechotta

    Department of Molecular Drug Design and Target Validation, Fraunhofer Institute for Cell Therapy and Immunology, Halle, Germany
    Contribution
    Resources, Writing - review and editing
    Competing interests
    No competing interests declared
  10. Matthias Prestel

    Institute for Stroke and Dementia Research (ISD), University Hospital, LMU, Munich, Germany
    Contribution
    Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
  11. Takashi Saito

    1. Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science Institute, Wako, Japan
    2. Department of Neurocognitive Science, Nagoya City University Graduate School of Medical Science, Nagoya, Japan
    Contribution
    Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Takaomi C Saido

    Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science Institute, Wako, Japan
    Contribution
    Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  13. Jochen Herms

    1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    2. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
    3. Center for Neuropathology and Prion Research, Ludwig-Maximilians-Universität München, Munich, Germany
    Contribution
    Methodology, Writing - review and editing
    Competing interests
    No competing interests declared
  14. Michael Willem

    Biomedical Center (BMC), Ludwig-Maximilians Universität München, Munich, Germany
    Contribution
    Formal analysis, Investigation, Writing - review and editing
    Competing interests
    No competing interests declared
  15. Christian Haass

    1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    2. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
    3. Biomedical Center (BMC), Ludwig-Maximilians Universität München, Munich, Germany
    Contribution
    Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  16. Stefan F Lichtenthaler

    1. German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    2. Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
    3. Neuroproteomics, School of Medicine, Klinikum Rechts der Isar, Technical University, Munich, Germany
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft
    For correspondence
    stefan.lichtenthaler@dzne.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-2211-2575
  17. Sabina Tahirovic

    German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft
    For correspondence
    sabina.tahirovic@dzne.de
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-4403-9559

Funding

Alzheimer Forschung Initiative (18014)

  • Sabina Tahirovic

Deutsche Forschungsgemeinschaft (EXC 2145 SyNergy ID 390857198)

  • Arthur Liesz
  • Jochen Herms
  • Christian Haass
  • Stefan F Lichtenthaler

Deutsche Forschungsgemeinschaft (Koselleck Project HA1737/16-1)

  • Christian Haass

H2020 European Research Council (ERC-StG 802305)

  • Arthur Liesz

Helmholtz-Gemeinschaft (ZT-0027)

  • Christian Haass

Bundesministerium für Bildung und Forschung (JPND PMG-AD)

  • Christian Haass
  • Stefan F Lichtenthaler

Bundesministerium für Bildung und Forschung (CLINSPECT-M)

  • Stefan F Lichtenthaler

NCL Foundation (01133)

  • Sabina Tahirovic

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We thank Allison Morningstar and Lina Dinkel for critically reading the manuscript. The authors are grateful to David Holtzman (Washington University School of Medicine, St Louis, Missouri, USA) for sharing the ApoE antibody, Stephan Schilling (Fraunhofer IZI-MWT, Halle/Saale, Germany) for the gift of the mouse monoclonal anti-pE-Aβ antibody J8, K Peter Nilsson (Linköping University, Sweden) for providing the LCOs and Mathias Jucker (Hertie-Institute for Clinical Brain Research, University of Tübingen, Germany) for providing the APPPS1 mice and assisting with the LCOs analysis. We thank Haike Hampel for excellent technical assistance.

Ethics

Animal experimentation: This study was performed in compliance with the German animal welfare law. All experimental procedures have been approved by the government of Upper Bavaria and covered by the general institutional allowance for breeding of transgenic animals (#04-26a) and thus did not need separate ethical approval.

Senior Editor

  1. Huda Y Zoghbi, Texas Children's Hospital, United States

Reviewing Editor

  1. Beth Stevens, Boston Children's Hospital, United States

Reviewers

  1. Beth Stevens, Boston Children's Hospital, United States
  2. Mathew Blurton-Jones
  3. Tara L Spires-Jones, University of Edinburgh, United Kingdom

Publication history

  1. Received: December 1, 2019
  2. Accepted: May 2, 2020
  3. Version of Record published: June 8, 2020 (version 1)

Copyright

© 2020, Sebastian Monasor et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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