Author response: Fibrillar Aβ triggers microglial proteome alterations and dysfunction in Alzheimer mouse models
2020-04-03

Article Figures and data Abstract eLife digest Introduction Results Discussion Materials and methods Data availability References Decision letter Author response Article and author information Metrics 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 Download asset Open asset 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. Figure 1—source data 1 Quality control of microglial isolation using MACS. FACS analysis of the CD11b-enriched (A) and CD11b-depleted fraction (B). Propidium Iodide (PI) was used to analyze cell viability (PI negative fraction). https://cdn.elifesciences.org/articles/54083/elife-54083-fig1-data1-v1.pdf Download elife-54083-fig1-data1-v1.pdf 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 Download asset Open asset 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. Figure 2—source data 1 Early, middle, and advanced MARPs. Protein groups are represented by the UniProt accession number. https://cdn.elifesciences.org/articles/54083/elife-54083-fig2-data1-v1.xlsx Download elife-54083-fig2-data1-v1.xlsx 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 Download asset Open asset 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, and (Figure the down-regulated middle MARPs, we identified additional and cell proteins like and (Figure Figure 2—source data a of key microglial functions throughout AD MARPs represent proteins that were only altered amyloid pathology. This group included up-regulation of proteins involved in such as and and down-regulation of proteins that play a role in the such as and (Figure Figure 2—source data 1). In addition, different receptor signaling proteins, including and also displayed a decreased abundance (Figure Furthermore, we observed a high of MARP signatures between the two models at 12 months (Figure 2—figure supplement A cluster enrichment analysis of middle and advanced MARPs identified down-regulation of biological processes including cell and as well as cell development and proliferation (Figure and C, Figure 3—figure supplement and F). we found an up-regulation of protein and (Figure Figure 3—figure supplement 1E and F). Additionally, alterations in processes and regulation were also detected (Figure These indicate that after an response, several processes to and phagocytosis are progressively increased Aβ Importantly, our proteomic analysis also detected alterations in proteins to different genetic risk factors of AD and Goate, including significantly increased levels of APOE, TREM2, and and decreased levels of and in both mouse models (Figure 2—figure supplement 2A and B). In addition, we compared MARP signatures with the previously single cell transcriptome study of mice (Keren-Shaul et al., 2017) to the as well as between proteomic and transcriptomic microglial profiles (Figure 2B–D). the proteome of 12 months old APPPS1 and APP-KI mice with the transcriptome of mice (Keren-Shaul et al., 2017), we were to and gene were only quantified on protein and transcript respectively (Figure of our proteomic signatures with the microglial transcripts (Keren-Shaul et al., 2017) revealed an overlap of and gene were only regulated on protein and transcript respectively (Figure 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 regulation compared to transcriptomic signatures such as the early MARP middle MARPs and or advanced MARPs and (Figure Figure 2—source data 1). Furthermore, the advanced MARP showed a consistent up-regulation in our proteomic analysis (Figure 2—source data previously as a homeostatic Figure Download asset Open asset of quantified and regulated proteins of APPPS1 and APP-KI mice at 12 months with single cell and of regulated proteins. (A) The of relatively quantified proteins of APPPS1 and APP-KI mice versus WT at 12 months a overlap with a single cell study (Keren-Shaul et al., 2017). of proteins of all quantified proteins) and their transcripts were quantified in all three data (B) The the overlap of significantly regulated proteins in APPPS1 and APP-KI mice versus WT at 12 months with regulated microglial transcripts (Keren-Shaul et al., 2017). which are for the proteome or transcriptome dataset are separately at the top or respectively. The indicated are on all relatively quantified proteins. (C) of proteomic data was performed using analysis of microglial lysates from 12 month old mice. This analysis revealed an increased abundance of the up-regulated MARPs APOE, TREM2, and as well as a decreased abundance of the down-regulated MARP in APPPS1 and APP-KI compared to WT microglia. indicate and Overall, our study a robust and method to microglial proteome and provides a resource that changes in brain immunity during different of Aβ accumulation. changes are detected in plaque-associated microglia Next, we proteomic changes by analysis using isolated microglia from 12 month old APPPS1 and APP-KI mice. This analysis the pronounced increase of the early MARPs and CD68, the middle MARPs and FABP5, as well as reduced levels of the advanced MARP (Figure in both mouse models compared to WT mice. Furthermore, proteomic changes were also by in order to of altered microglial proteins in APPPS1 and APP-KI mice. analysis of 3 month old APPPS1 mice already revealed increased of selected MARPs such as (Figure (Figure supplement and (Figure supplement that stages of microglial in This increase was detected in positive microglia that were clustering amyloid plaques, but not in microglia from plaques and was – in with our proteomic data – less pronounced in 3 month old APP-KI mice. Accordingly, at 12 both APPPS1 and APP-KI mice showed a similar increase in the levels of selected MARP (Figure and decreased levels of (Figure compared to the WT mice, in microglia amyloid plaques. we selected microglial proteomic alterations from our dataset by and In addition, we the kinetic differences in AD-associated proteomic signatures of APPPS1 and APP-KI microglia. Our data that between microglia and Aβ is triggering the proteomic changes as they could be observed in plaque-associated microglial Figure with 2 supplements see all Download asset Open asset Microglial is up-regulated in 3 month old APPPS1 mice. analysis of reveals an increased abundance of this early MARP in positive APPPS1 microglia Aβ plaques that is less prominent in APP-KI microglia. is detected in WT microglia. was used for in are shown with a higher in lower Figure 6 Download asset Open asset Microglial is increased in both AD mouse models at 12 months of age. analysis an up-regulation of in positive APPPS1 and APP-KI microglia Aβ plaques compared to WT is was used for in are shown with a higher in lower Figure Download asset Open asset Microglial is down-regulated in both AD mouse models at 12 months of age. analysis of a of this homeostatic in positive WT microglia. In the APPPS1 and APP-KI mice, is reduced in positive microglia Aβ plaques Of note, increased was found in the of amyloid plaques in APPPS1 and APP-KI mice. was used for in are shown with a higher in lower and reduced levels of in plaque microglia. APPPS1 and APP-KI mice show similar dynamics of amyloid plaque but in plaque The of proteomic microglial changes was found to correlate with Aβ plaque accumulation throughout disease However, the of MARP signatures between the models and earlier in the APPPS1 mice (Figure and Figure the comparable plaque observed in both mouse models (Figure 1—figure supplement 2). Thus, it appears that the of amyloid plaques is different between the APPPS1 and APP-KI mice. To we analyzed amyloid plaques in 3, 6 and 12 month old APPPS1 and APP-KI mice by We used the et al., to Aβ plaques, and to fibrillar amyloid (Daria et al., 2017; Figure In with amyloid plaque pathology reported in this model (Radde et al., 2006), APPPS1 mice fibrillar