Microglia are brain parenchymal macrophages that are unique among tissue-resident macrophages due to their primitive yolk sack origin, self-renewal properties (Ajami et al., 2007), and independence from adult hematopoiesis (Ginhoux et al., 2010; Schulz et al., 2012; Sheng et al., 2015). These cells are essential for normal neuronal development, brain function, and central nervous system (CNS) homeostasis, and dysfunction is associated with severe brain neuropathologies (Leng and Edison, 2021; Sevenich, 2018). In neurodegenerative diseases, such as Alzheimer’s disease (AD), microglia form a barrier around amyloid plaques, thereby protecting against the neurotoxicity of aggregated beta-amyloid (Aβ) (Condello et al., 2015). Disruption of microglial activity, which is often related to aging and inflammation, can promote this neurotoxicity (Salter and Stevens, 2017) and affect the clearance of Aβ aggregates (Floden and Combs, 2011). The resulting increase in plaque formation (Spangenberg et al., 2019) leads to the progression of pathogenesis.

In several neurodegenerative diseases, the gradual appearance of a subpopulation of CD11c⁺ activated microglia, also known as disease-associated microglia or neurodegenerative microglia, correlates with the progression of disease pathology (Deczkowska et al., 2018; Keren-Shaul et al., 2017; Krasemann et al., 2017). In AD, the accumulation of these highly phagocytic cells around the Aβ deposits (Kamphuis et al., 2016; Yin et al., 2017) mitigates Aβ-associated tau seeding and spreading (Gratuze et al., 2021). These activated microglia may play a protective role in neurodegeneration, thus implicating targeted manipulation as a therapeutic strategy. Although the origin of the myeloid cells that accumulate around the Aβ deposits is controversial, early studies indicated selective migration of monocytic cells toward the Aβ plaques (Hohsfield and Humpel, 2015), while more recent research suggests that these cells are derived from resident embryonic-derived microglia (Reed-Geaghan et al., 2020; Shukla et al., 2019).

In addition to the macrophages in brain parenchyma, microglia-independent macrophages are also found at the border regions, such as the subdural meninges (SDM), including the pia and arachnoid mater, the dura mater (DM), and the choroid plexus (CP) (Brioschi et al., 2020; Utz et al., 2020). Single-cell RNAseq analysis revealed that these border-associated macrophages (BAMs) represent a family of different subpopulations (Van Hove et al., 2019). However, the contributions of BAMs to CNS integrity and neurodegeneration remain to be elucidated.

To investigate the influence of AD on the macrophage CNS landscape, we analyzed the phenotype and turnover kinetics of different myeloid cells in the brain parenchyma and border-associated tissues. We used a novel AD mouse model generated by back-crossing App^NL-G-F knock-in (APP-KI) mice (Saito et al., 2014) with a Kit^MerCreMer/R26^YFP fate-mapping mouse strain (Sheng et al., 2015) to monitor bone marrow (BM)-driven replenishment of brain myeloid cells during disease progression. We also investigated the origins of CD11c⁺ microglia since the ontogeny of ‘activated’ microglia in AD is still a matter of debate.

In this study, we conducted a comprehensive fate-mapping analysis of murine brain microglia and BAMs and their turnover kinetics during the progression of AD.

Mouse models of AD have been instrumental in clarifying the cellular and molecular mechanisms underlying this irreversible brain disorder. Transgenic mice overexpressing proteins linked to familial AD (5xFAD), single mutant amyloid precursors (APP), or double mutant APP and presenilin (APP-PS1) have been used in many studies (Sasaguri et al., 2017). In our study, we exploited an APP-KI mouse strain expressing a mutant form of humanized APP, the parent protein of Aβ, knocked in under the control of the endogenous promoter. This model avoids some disadvantages associated with transgenic APP models, including artifacts caused by overexpression of other APP fragments in addition to Aβ, non-physiological cell-type expression, and potential insertion site disruption (Saito et al., 2014). The APP-KI mouse line expresses three human AD-associated mutations that promote the progressive accumulation of Aβ through its increased production of Aβ, particularly the more toxic Aβ42 form, as well as increasing Aβ aggregation and reducing degradation. APP-KI mice develop progressive Aβ accumulation from 2 months of age, thus mimicking several aspects of human AD, including microgliosis and synaptic loss (Figure 1—figure supplement 1A).

First, we characterized the myeloid cell landscape in different brain regions of healthy young WT and APP-KI mice (2 months) as well as aged WT and APP-KI mice (12 months) (Figure 1—figure supplement 2). In our multiparameter flow cytometry and uniform manifold approximation and projection analyses, we included a panel of myeloid markers to delineate microglia, activated CD11c⁺ microglia, monocyte-derived macrophages (MdCs), F4/80^intCD11a⁺ infiltrating macrophages (Shukla et al., 2019), border-associated macrophages (BAMs), neutrophils, monocytes and, eosinophils.

P2RY12⁺ microglia were the main CD45^int F4/80^hi cell population in the brain parenchyma of all mice and their absolute numbers are significantly enhanced in brains of 12-month-old APP-KI mice due to microgliosis occurring during the development of AD (Sevenich, 2018). AD mice showed also a substantial increase in the absolute numbers of P2RY12^lowCD11c⁺ activated microglia, which were absent in the brain of young mice and constituted only a minor fraction in aged mice (Figure 1A–C and E). Immunofluorescent analysis showed that CD11c⁺ activated microglia aggregate around Aβ amyloid plaques (Figure 1D) In accordance with reports of the appearance of activated microglia during neurodegeneration in other AD transgenic mouse models, such as 5xFAD and APP/PS1 (Keren-Shaul et al., 2017; Mrdjen et al., 2018), this phenotype was recapitulated in APP-KI mice in our study (Figure 1—figure supplement 1B,C). The remaining myeloid cell subsets detected in the brain parenchyma, such as neutrophils (Ly6G⁺), monocytes (Ly6C^hi), F4/80^int Ly6C^negMHCII^hi MdCs, peripheral-derived myeloid F4/80^intMHCII⁻CD11a⁺ cells (Shukla et al., 2019), eosinophils (Siglec-F⁺), and cDCs (CD11c^hiMHCII^hi), were mainly restricted to the CD45^hi gate (Figure 1A). Surprisingly, we did not observe an enhanced infiltration of BM-derived inflammatory cells in the brain during disease progression. Even in aged APP-KI mice, the infiltration by Ly6C^hi monocytes and Ly6G⁺ neutrophils was comparable to that in age-matched controls (Figure 1A–B and E).

Figure 1 with 2 supplements see all

Download asset Open asset

Figure 1—source data 1

Absolute numbers of different myeloid cell populations within the total parenchymal brain CD45⁺ cell population.

https://cdn.elifesciences.org/articles/71879/elife-71879-fig1-data1-v2.xlsx

Download elife-71879-fig1-data1-v2.xlsx

In all three separately analyzed CNS border-associated tissues, a predominant F4/80^hiCD206⁺ BAM fraction could be further separated into MHCII⁺ and MHCII^low subpopulations, which is consistent with previous reports (Van Hove et al., 2019; Figure 2A–D, Figure 2—figure supplement 1). A large population of CD206⁺MHCII^low BAMs was preferentially localized in the SDM and CP, whereas these cells were less abundant in the DM. Interestingly, a significant transition from CD206⁺MHCII^low to CD206⁺MHCII⁺ BAMs was observed with aging and AD progression (Figure 2B and C). Similarly, increased MHCII⁺ CNS-associated macrophages were also observed during EAE induced neuroinflammation (Jordao et al., 2019). All other myeloid cell populations, including neutrophils, monocytes, MdCs, CD11a⁺ cells, eosinophils, and DCs, were present in each tissue, although at different frequencies (Figure 2A–D, Figure 2—figure supplements 1 and 2). Similar to the brain parenchyma, monocyte and neutrophil frequencies were not augmented in any of the three border regions of the aged WT and APP-KI mice, which excludes the possibility that an enhanced inflammatory cell infiltration is caused by aging or neurodegenerative disease progression (Figure 2A–C, Figure 2—figure supplements 1 and 2).

Figure 2 with 2 supplements see all

Download asset Open asset

Figure 2—source data 1

Frequency of BAMs (CD206⁺MHCII^low and CD206⁺MHCII⁺), Mdc, and monocytes within the total non-parenchymal CD45⁺ cell population of the DM, SDM, and CP.

https://cdn.elifesciences.org/articles/71879/elife-71879-fig2-data1-v2.xlsx

Download elife-71879-fig2-data1-v2.xlsx

To investigate the origins and replenishment kinetics of distinct brain macrophage subpopulations, we crossed the APP-KI mouse line with the Kit^MerCreMer/R26^YFP fate-mapping mouse strain. The resulting APP-KI/Kit^MerCreMer/R26^YFP inducible adult fate-mapping mouse model allowed us to irreversibly label Kit-expressing BM-progenitors and trace them in different brain regions during the progression of AD. To induce the YFP label in Kit-expressing BM progenitor cells, APP-KI/Kit^MerCreMer/R26^YFP mice and the corresponding Kit^MerCreMer/R26^YFP controls were administered TAM at 2, 4, 6, and 8 months of age. Parenchymal and non-parenchymal brain cells were isolated separately and analyzed by multicolor flow cytometry.

In the parenchyma of 10-month-old mice, a strong YFP signal was limited to CD45^hi cells and was barely detectable in the CD45^int fraction, which included the microglia and activated CD11c⁺ microglia, in both WT and AD mice (Figure 3A–C). In fact, microglia showed minimal YFP-labeling, which further confirmed their embryonic origin and BM-independence (Sheng et al., 2015). During AD progression, microglial YFP-labeling remained minimal, indicating that the ongoing neurodegeneration did not promote their replenishment by BM-derived cells. Similarly, activated CD11c⁺ microglia, which were increasingly formed during AD progression (Figure 1—figure supplement 1B-C), maintained a low YFP-labeling profile, suggesting that despite their phenotypic shift, these cells preserved their embryonic signature and were not replaced by BM-derived cells during evolution of the disease (Figure 3A–C). Differently, the kinetics of monocyte replacement was extremely rapid, with all monocytes YFP-labeled 2 months after induction, indicating that these cells are exclusively BM-derived (Figure 3A and B, lower panel, middle). In contrast, fetal-derived MdCs were gradually replaced by BM-derived cells over time and were fully replenished at 8 months post-induction, with no significant difference between WT and AD mice (Figure 3A and B, lower panel, left). Likewise, CD11a⁺ macrophages were clearly BM-derived and showed high YFP-labeling and turnover kinetics comparable to monocytes and neutrophils (Figure 3A and B, lower panel, right).

Figure 3

Download asset Open asset

Figure 3—source data 1

Frequency of YFP⁺ parenchymal microglia, activated CD11c⁺ microglia, MdCs, monocytes, and CD11a⁺ cells.

https://cdn.elifesciences.org/articles/71879/elife-71879-fig3-data1-v2.xlsx

Download elife-71879-fig3-data1-v2.xlsx

To verify whether activated CD11c⁺ microglia retained their fetal lineage, we performed E7.5 embryonic labeling of APP-KI/Kit^MerCreMer/R26^YFP mice and analyzed microglia and activated CD11c⁺ microglia obtained from disease-affected offspring aged 9 months. Despite the AD-associated pathology, we not only confirmed that activated CD11c⁺ microglia maintained their embryonic origin, but showed that microglia and activated CD11c⁺ microglia share the same yolk sac origin, whereas all other tested myeloid cells (monocytes, Mdc, CD11a⁺ macrophages, and neutrophils) did not arise from embryonic progenitors of the yolk sac (Figure 3D).

Using our fate-mapping mouse model, we then analyzed myeloid cells residing in distinct CNS border-associated regions. In adult fate-mapping, non-parenchymal monocytes, MdCs, and CD11a⁺ macrophages were swiftly replaced by BM cells, with 100% YFP-positivity at 2 months post-induction (Figure 4, right, Figure 4—figure supplement 1). In contrast, both CD206⁺ BAM subpopulations showed weaker YFP-labeling compared to other macrophages such as MdCs, which were already fully replenished within 2 months in all three regions (Figure 4). In the DM, YFP-labeling was significantly higher in CD206⁺MHCII⁺ BAMs than that in CD206⁺MHCII^low BAMs (Figure 4, upper panel). Dural CD206⁺MHCII⁺ BAMs were characterized by a slow, but consistent BM-cell replacement over time, reaching approximately 35% YFP-positivity by 8 months post-induction. In contrast, the turnover rate of both BAM populations located in the SDM was markedly slower (Figure 4, middle panel), which suggests that their niche is less accessible to BM-dependent replenishment. With its fenestrated blood vessels, the DM has a greater capacity to support peripheral cell traffic. In addition, skull BM-derived monocytes, which seed DM through tiny vascular connecting corridors (Cugurra et al., 2021), can also contribute to the refilling of dural BAMs. In contrast, the strong tight junctions of the blood vessels in the SDM limit cell exchange with the periphery (Mastorakos and McGavern, 2019). Previous studies have shown a rapid BAM turnover in the CP supported by partial input from the circulation (Goldmann et al., 2016; Van Hove et al., 2019). However, although both MHCII^low and MHCII⁺CD206⁺ BAMs displayed a dual origin in our fate-mapping mouse model, their replenishment from BM-derived cells was extremely slow, with the YFP signal reaching only 10–15% by 8 months after induction. More than 85% of CP BAMs retained their embryonic phenotype (Figure 4, lower panel). Consistent with the lowest levels of YFP labeling observed in MHCII^lowCD206⁺ BAMs located in SDM and CP (Figure 4, left panels) we found a significantly increased YFP labeling only in this particular BAM subpopulation obtained from the progeny of E7.5 TAM-treated mice (SDM MHCII^lowCD206⁺ BAMs: 10%; CP MHCII^lowCD206⁺ BAMs: 15%) (Figure 4—figure supplement 2). These data indicate that some of these SDM and CP MHCII^lowCD206⁺ BAMs are descendants of yolk sac precursors.

Figure 4 with 2 supplements see all

Download asset Open asset

Figure 4—source data 1

Percentages of YFP⁺ non-parenchymal BAMs (CD206⁺MHCII^low and CD206⁺MHCII⁺ cells) and MdCs.

https://cdn.elifesciences.org/articles/71879/elife-71879-fig4-data1-v2.xlsx

Download elife-71879-fig4-data1-v2.xlsx

The lack of a significant difference in the YFP-labeling profiles of BAMs from healthy or AD mice indicates that the development of AD neither supports nor accelerates BAM replacement by BM-progenitors (Figure 4).

The contribution of monocytes to the pool of microglia is still unclear. The monocyte-to-microglia transition occurs in the brain, but only under certain conditions of inflammation or injury, such as meningitis (Djukic et al., 2006) and neonatal stroke (Chen et al., 2020). However, the original yolk sac embryonic microglia identity is preserved during experimental autoimmune encephalomyelitis (Ajami et al., 2011; Jordao et al., 2019), although infiltrating monocytes are recruited to the brain during the progression of this neuroinflammatory disease. In an elegant parabiosis experiment using two different mouse models of AD (APPPS1-21 and 5xFAD), it was shown that peripheral monocytes do not contribute to the microglial pool surrounding the Aβplaques (Wang et al., 2016). Here, we extended our analysis and demonstrated that, in the steady state, microglia, activated CD11c⁺ microglia and BAMs are a remarkably stable embryonic-derived population and their turnover remains unaffected in AD; the progression of this neurodegenerative disease does not promote or sustain their replacement by BM-derived progenitors. Furthermore, we confirmed that the transformation from homeostatic microglia to activated CD11c⁺ microglia is not mediated by infiltrating inflammatory BM-marrow derived cells, but imprinted by the local brain microenvironment that is severely perturbed by the AD pathology. This new understanding will be valuable in manipulating the transition of microglia to activated microglia as a therapeutic strategy in AD.

All data generated or analysed during this study are included in the manuscript and supporting files. Source data files have been provided for Figures 1, 2, 3 and 4 and figure supplements.

1. 1. Ajami B
   2. Bennett JL
   3. Krieger C
   4. Tetzlaff W
   5. Rossi FM

   (2007) Local self-renewal can sustain CNS microglia maintenance and function throughout adult life

   Nature Neuroscience 10:1538–1543.

   https://doi.org/10.1038/nn2014

   • Google Scholar
2. 1. Ajami B
   2. Bennett JL
   3. Krieger C
   4. McNagny KM
   5. Rossi FM

   (2011) Infiltrating monocytes trigger EAE progression, but do not contribute to the resident microglia pool

   Nature Neuroscience 14:1142–1149.

   https://doi.org/10.1038/nn2887

   • Google Scholar
3. 1. Brioschi S
   2. Zhou Y
   3. Colonna M

   (2020) Brain Parenchymal and Extraparenchymal Macrophages in Development, Homeostasis, and Disease

   Journal of Immunology 204:294–305.

   https://doi.org/10.4049/jimmunol.1900821

   • Google Scholar
4. 1. Chen HR
   2. Sun YY
   3. Chen CW
   4. Kuo YM
   5. Kuan IS
   6. Tiger Li ZR
   7. Short-Miller JC
   8. Smucker MR
   9. Kuan C

   (2020) Fate mapping via CCR2-CreER mice reveals monocyte-to-microglia transition in development and neonatal stroke

   Science Advances 6:eabb2119.

   https://doi.org/10.1126/sciadv.abb2119

   • Google Scholar
5. 1. Condello C
   2. Yuan P
   3. Schain A
   4. Grutzendler J

   (2015) Microglia constitute a barrier that prevents neurotoxic protofibrillar Abeta42 hotspots around plaques

   Nature Communications 6:6176.

   https://doi.org/10.1038/ncomms7176

   • Google Scholar
6. 1. Cugurra A
   2. Mamuladze T
   3. Rustenhoven J
   4. Dykstra T
   5. Beroshvili G
   6. Greenberg ZJ
   7. Baker W
   8. Papadopoulos Z
   9. Drieu A
   10. Blackburn S
   11. Kanamori M
   12. Brioschi S
   13. Herz J
   14. Schuettpelz LG
   15. Colonna M
   16. Smirnov I
   17. Kipnis J

   (2021) Skull and vertebral bone marrow are myeloid cell reservoirs for the meninges and CNS parenchyma

   Science 373:eabf7844.

   https://doi.org/10.1126/science.abf7844

   • Google Scholar
7. 1. Deczkowska A
   2. Keren-Shaul H
   3. Weiner A
   4. Colonna M
   5. Schwartz M
   6. Amit I

   (2018) Disease-Associated Microglia: A Universal Immune Sensor of Neurodegeneration

   Cell 173:1073–1081.

   https://doi.org/10.1016/j.cell.2018.05.003

   • Google Scholar
8. 1. Djukic M
   2. Mildner A
   3. Schmidt H
   4. Czesnik D
   5. Bruck W
   6. Priller J
   7. Nau R
   8. Prinz M

   (2006) Circulating monocytes engraft in the brain, differentiate into microglia and contribute to the pathology following meningitis in mice

   Brain 129:2394–2403.

   https://doi.org/10.1093/brain/awl206

   • Google Scholar
9. 1. Floden AM
   2. Combs CK

   (2011) Microglia demonstrate age-dependent interaction with amyloid-beta fibrils

   Journal of Alzheimer’s Disease 25:279–293.

   https://doi.org/10.3233/JAD-2011-101014

   • Google Scholar
10. 1. Ginhoux F
    2. Greter M
    3. Leboeuf M
    4. Nandi S
    5. See P
    6. Gokhan S
    7. Mehler MF
    8. Conway SJ
    9. Ng LG
    10. Stanley ER
    11. Samokhvalov IM
    12. Merad M

    (2010) Fate mapping analysis reveals that adult microglia derive from primitive macrophages

    Science 330:841–845.

    https://doi.org/10.1126/science.1194637

    • PubMed
    • Google Scholar
11. 1. Goldmann T
    2. Wieghofer P
    3. Jordao MJ
    4. Prutek F
    5. Hagemeyer N
    6. Frenzel K
    7. Amann L
    8. Staszewski O
    9. Kierdorf K
    10. Krueger M
    11. Locatelli G
    12. Hochgerner H
    13. Zeiser R
    14. Epelman S
    15. Geissmann F
    16. Priller J
    17. Rossi FM
    18. Bechmann I
    19. Kerschensteiner M
    20. Linnarsson S
    21. Jung S
    22. Prinz M

    (2016) Origin, fate and dynamics of macrophages at central nervous system interfaces

    Nature Immunology 17:797–805.

    https://doi.org/10.1038/ni.3423

    • Google Scholar
12. 1. Gratuze M
    2. Chen Y
    3. Parhizkar S
    4. Jain N
    5. Strickland MR
    6. Serrano JR
    7. Colonna M
    8. Ulrich JD
    9. Holtzman DM

    (2021) Activated microglia mitigate Abeta-associated tau seeding and spreading

    The Journal of Experimental Medicine 218:e20210542.

    https://doi.org/10.1084/jem.20210542

    • Google Scholar
13. 1. Hohsfield LA
    2. Humpel C

    (2015) Migration of blood cells to beta-amyloid plaques in Alzheimer’s disease

    Experimental Gerontology 65:8–15.

    https://doi.org/10.1016/j.exger.2015.03.002

    • Google Scholar
14. 1. Jordao MJC
    2. Sankowski R
    3. Brendecke SM
    4. Sagar LG
    5. Tai YH
    6. Tay TL
    7. Schramm E
    8. Armbruster S
    9. Hagemeyer N
    10. Gross O
    11. Mai D
    12. Cicek O
    13. Falk T
    14. Kerschensteiner M
    15. Grun D
    16. Prinz M

    (2019) Single-cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation

    Science 363:aat7554.

    https://doi.org/10.1126/science.aat7554

    • Google Scholar
15. 1. Kamphuis W
    2. Kooijman L
    3. Schetters S
    4. Orre M
    5. Hol EM

    (2016) Transcriptional profiling of CD11c-positive microglia accumulating around amyloid plaques in a mouse model for Alzheimer’s disease

    Biochimica et Biophysica Acta 1862:1847–1860.

    https://doi.org/10.1016/j.bbadis.2016.07.007

    • Google Scholar
16. 1. Keren-Shaul H
    2. Spinrad A
    3. Weiner A
    4. Matcovitch-Natan O
    5. Dvir-Szternfeld R
    6. Ulland TK
    7. David E
    8. Baruch K
    9. Lara-Astaiso D
    10. Toth B
    11. Itzkovitz S
    12. Colonna M
    13. Schwartz M
    14. Amit I

    (2017) A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease

    Cell 169:1276–1290.

    https://doi.org/10.1016/j.cell.2017.05.018

    • Google Scholar
17. 1. Krasemann S
    2. Madore C
    3. Cialic R
    4. Baufeld C
    5. Calcagno N
    6. El Fatimy R
    7. Becker L
    8. O’Loughlin E
    9. Xu Y
    10. Fanek Z
    11. Greco DJ
    12. Smith ST
    13. Tweet G
    14. Humulock Z
    15. Zrzavy T
    16. Conde-Sanroman P
    17. Gacias M
    18. Weng Z
    19. Chen H
    20. Tjon E
    21. Mazaheri F
    22. Hartmann K
    23. Madi A
    24. Ulrich JD
    25. Glatzel M
    26. Worthmann A
    27. Heeren J
    28. Budnik B
    29. Lemere C
    30. Ikezu T
    31. Heppner FL
    32. Litvak V
    33. Holtzman DM
    34. Lassmann H
    35. Weiner HL
    36. Ochando J
    37. Haass C
    38. Butovsky O

    (2017) The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases

    Immunity 47:566–581.

    https://doi.org/10.1016/j.immuni.2017.08.008

    • Google Scholar
18. 1. Leng F
    2. Edison P

    (2021) Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here

    Nature Reviews. Neurology 17:157–172.

    https://doi.org/10.1038/s41582-020-00435-y

    • Google Scholar
19. 1. Mastorakos P
    2. McGavern D

    (2019) The anatomy and immunology of vasculature in the central nervous system

    Science Immunology 4:eaav0492.

    https://doi.org/10.1126/sciimmunol.aav0492

    • Google Scholar
20. 1. Mrdjen D
    2. Pavlovic A
    3. Hartmann FJ
    4. Schreiner B
    5. Utz SG
    6. Leung BP
    7. Lelios I
    8. Heppner FL
    9. Kipnis J
    10. Merkler D
    11. Greter M
    12. Becher B

    (2018) High-Dimensional Single-Cell Mapping of Central Nervous System Immune Cells Reveals Distinct Myeloid Subsets in Health, Aging, and Disease

    Immunity 48:380–395.

    https://doi.org/10.1016/j.immuni.2018.01.011

    • Google Scholar
21. 1. Reed-Geaghan EG
    2. Croxford AL
    3. Becher B
    4. Landreth GE

    (2020) Plaque-associated myeloid cells derive from resident microglia in an Alzheimer’s disease model

    The Journal of Experimental Medicine 217:e20191374.

    https://doi.org/10.1084/jem.20191374

    • Google Scholar
22. 1. Saito T
    2. Matsuba Y
    3. Mihira N
    4. Takano J
    5. Nilsson P
    6. Itohara S
    7. Iwata N
    8. Saido TC

    (2014) Single App knock-in mouse models of Alzheimer’s disease

    Nature Neuroscience 17:661–663.

    https://doi.org/10.1038/nn3697

    • Google Scholar
23. 1. Salter MW
    2. Stevens B

    (2017) Microglia emerge as central players in brain disease

    Nature Medicine 23:1018–1027.

    https://doi.org/10.1038/nm.4397

    • Google Scholar
24. 1. Sasaguri H
    2. Nilsson P
    3. Hashimoto S
    4. Nagata K
    5. Saito T
    6. De Strooper B
    7. Hardy J
    8. Vassar R
    9. Winblad B
    10. Saido T

    (2017) APP mouse models for Alzheimer’s disease preclinical studies

    The EMBO Journal 36:2473–2487.

    https://doi.org/10.15252/embj.201797397

    • Google Scholar
25. 1. Schulz C
    2. Gomez Perdiguero E
    3. Chorro L
    4. Szabo-Rogers H
    5. Cagnard N
    6. Kierdorf K
    7. Prinz M
    8. Wu B
    9. Jacobsen SE
    10. Pollard JW
    11. Frampton J
    12. Liu KJ
    13. Geissmann F

    (2012) A lineage of myeloid cells independent of Myb and hematopoietic stem cells

    Science 336:86–90.

    https://doi.org/10.1126/science.1219179

    • Google Scholar
26. 1. Sevenich L

    (2018) Brain-Resident Microglia and Blood-Borne Macrophages Orchestrate Central Nervous System Inflammation in Neurodegenerative Disorders and Brain Cancer

    Frontiers in Immunology 9:697.

    https://doi.org/10.3389/fimmu.2018.00697

    • Google Scholar
27. 1. Sheng J
    2. Ruedl C
    3. Karjalainen K

    (2015) Most Tissue-Resident Macrophages Except Microglia Are Derived from Fetal Hematopoietic Stem Cells

    Immunity 43:382–393.

    https://doi.org/10.1016/j.immuni.2015.07.016

    • Google Scholar
28. 1. Shukla A
    2. McIntyre LL
    3. Marsh SE
    4. Schneider CA
    5. Hoover EM
    6. Walsh CM
    7. Lodoen M
    8. Blurton-Jones M
    9. Inlay MA

    (2019) CD11a expression distinguishes infiltrating myeloid cells from plaque-associated microglia in Alzheimer’s disease

    Glia 67:844–856.

    https://doi.org/10.1002/glia.23575

    • Google Scholar
29. 1. Spangenberg E
    2. Severson PL
    3. Hohsfield LA
    4. Crapser J
    5. Zhang J
    6. Burton EA
    7. Zhang Y
    8. Spevak W
    9. Lin J
    10. Phan NY
    11. Habets G
    12. Rymar A
    13. Tsang G
    14. Walters J
    15. Nespi M
    16. Singh P
    17. Broome S
    18. Ibrahim P
    19. Zhang C
    20. Bollag G
    21. West BL
    22. Green KN

    (2019) Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer’s disease model

    Nature Communications 10:3758.

    https://doi.org/10.1038/s41467-019-11674-z

    • Google Scholar
30. 1. Utz SG
    2. See P
    3. Mildenberger W
    4. Thion MS
    5. Silvin A
    6. Lutz M
    7. Ingelfinger F
    8. Rayan NA
    9. Lelios I
    10. Buttgereit A
    11. Asano K
    12. Prabhakar S
    13. Garel S
    14. Becher B
    15. Ginhoux F
    16. Greter M

    (2020) Early Fate Defines Microglia and Non-parenchymal Brain Macrophage Development

    Cell 181:557–573.

    https://doi.org/10.1016/j.cell.2020.03.021

    • PubMed
    • Google Scholar
31. 1. Van Hove H
    2. Martens L
    3. Scheyltjens I
    4. De Vlaminck K
    5. Pombo Antunes AR
    6. De Prijck S
    7. Vandamme N
    8. De Schepper S
    9. Van Isterdael G
    10. Scott CL
    11. Aerts J
    12. Berx G
    13. Boeckxstaens GE
    14. Vandenbroucke RE
    15. Vereecke L
    16. Moechars D
    17. Guilliams M
    18. Van Ginderachter JA
    19. Saeys Y
    20. Movahedi K

    (2019) A single-cell atlas of mouse brain macrophages reveals unique transcriptional identities shaped by ontogeny and tissue environment

    Nature Neuroscience 22:1021–1035.

    https://doi.org/10.1038/s41593-019-0393-4

    • Google Scholar
32. 1. Wang Y
    2. Ulland TK
    3. Ulrich JD
    4. Song W
    5. Tzaferis JA
    6. Hole JT
    7. Yuan P
    8. Mahan TE
    9. Shi Y
    10. Gilfillan S
    11. Cella M
    12. Grutzendler J
    13. DeMattos RB
    14. Cirrito JR
    15. Holtzman DM
    16. Colonna M

    (2016) TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques

    The Journal of Experimental Medicine 213:667–675.

    https://doi.org/10.1084/jem.20151948

    • Google Scholar
33. 1. Yin Z
    2. Raj D
    3. Saiepour N
    4. Van Dam D
    5. Brouwer N
    6. Holtman IR
    7. Eggen BJL
    8. Moller T
    9. Tamm JA
    10. Abdourahman A
    11. Hol EM
    12. Kamphuis W
    13. Bayer TA
    14. De Deyn PP
    15. Boddeke E

    (2017) Immune hyperreactivity of Abeta plaque-associated microglia in Alzheimer’s disease

    Neurobiology of Aging 55:115–122.

    https://doi.org/10.1016/j.neurobiolaging.2017.03.021

    • Google Scholar

Author details

1. Xiaoting Wu

   School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

   Contribution

   Investigation, Methodology, Visualization

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0281-8717

2. Takashi Saito

   Department of Neurocognitive Science, Institute of Brain Science, Nagoya City University Graduate School of Medical Sciences, Nagoya, Japan

   Contribution

   Resources

   Competing interests

   No competing interests declared

3. Takaomi C Saido

   Laboratory for Proteolytic Neuroscience, RIKEN Center for Brain Science, Wako-Shi, Japan

   Contribution

   Resources

   Competing interests

   No competing interests declared

4. Anna M Barron

   Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore

   Contribution

   Investigation, Methodology, Writing - review and editing

   Competing interests

   No competing interests declared

5. Christiane Ruedl

   School of Biological Sciences, Nanyang Technological University, Singapore, Singapore

   Contribution

   Conceptualization, Formal analysis, Funding acquisition, Project administration, Supervision, Validation, Visualization, Writing - original draft, Writing - review and editing

   For correspondence

   ruedl@ntu.edu.sg

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5599-6541

• 3,589

  views

• 430

  downloads

• 26

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.