Microglia aggregates define distinct immune and neurodegenerative niches in Alzheimer's disease hippocampus.

doi:10.21203/rs.3.rs-5387511/v1

Download PDF

Article

Microglia aggregates define distinct immune and neurodegenerative niches in Alzheimer's disease hippocampus.

https://doi.org/10.21203/rs.3.rs-5387511/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 15 Feb, 2025

Read the published version in Acta Neuropathologica →

You are reading this latest preprint version

In Alzheimer's disease (AD), microglia show strong associations with amyloid-β (Aβ) and tau pathology, forming cellular aggregates such as Aβ plaque-associated microglia (PaM). Using high-content neuropathology, we found another type of microglial aggregates, morphologically distinct and not associated with Aβ plaques, mainly localised in the pyramidal layer of the CA2/CA1 human hippocampal subfields of AD patients, namely Coffin-like microglia (CoM). This study aims to define the morphological, pathological and molecular signatures of hippocampal PaM and CoM in AD patients and their implication in disease progression. We mapped and profiled PaM, CoM and their neuropathological and astrocytic microenvironment using Nanostring GeoMx Deep Spatial Profiling (DSP), multiplex chromogenic and confocal microscopy in AD hippocampal post-mortem samples. Key markers and results were validated in a collection of AD, DLB and age-matched control samples. CoM, found attached to tau tangles and neurons with phosphorylated α-synuclein accumulations, displayed specific protein and transcriptomic signatures associated with STING, protein degradation, TGF-β, and NF-κB signalling pathways. In contrast, PaM and PaM-astrocyte signatures were associated with complement system pathways, ErbB signalling, metabolic and neurodegenerative activities. While no direct association of CD8 + T cells with either PaM or CoM was observed, CD163 + perivascular macrophages were frequently found incorporated into PaM. This study provides new insights into the molecular characteristics of microglia and their association with astrocytes and infiltrating immune cells to delineate specific neurodegenerative hotspots in AD and related dementia and highlights their prominent role in hippocampal deterioration.

Spatial profiling

Alzheimer's Disease

hippocampus

glial cells

proteinopathies

peripheral immune cells

Alzheimer's disease (AD) is a multifaceted disease characterised by numerous molecular and cellular alterations that remain incompletely understood. Recognised as the most common age-related neurodegenerative disease (NDD) and the leading cause of dementia, AD is characterised by a progressive and irreversible decline in cognitive function, with memory loss as one of the most prominent clinical symptoms. The cellular underpinnings of memory loss are linked to an increased vulnerability of anatomical brain regions associated with memory, such as the hippocampus, to AD [1, 3, 36, 42, 47, 64, 72, 104]. Memory-related brain regions are particularly affected by atrophy, neuronal and synaptic loss, neuroinflammation and high burdens of extracellular amyloid-β (Aβ) plaques and intraneuronal hyperphosphorylated tau (pTau) neurofibrillary tangles (NFTs), the latter two being the neuropathological hallmarks of AD. Co-occurrence with other misfolded protein pathologies, such as phosphorylated α-synuclein (pSyn) typical of Parkinson’s disease (PD), or TAR DNA-binding protein 43 (TDP-43), found in pathological deposit in amyotrophic lateral sclerosis (ALS) or frontotemporal dementia (FTD) is frequently observed [26, 33, 51, 52, 54, 66, 70].

While traditionally considered secondary players in neurodegenerative processes, the roles of glial cells in AD progression have received increasing attention. Microglia, the immune cells of the central nervous system (CNS), are critical for maintaining brain homeostasis [23, 75, 81]. In AD, they may be directly involved in neurodegeneration. Genome-wide association studies (GWAS) identified AD risk factors associated with microglial function such as TREM2, ApoE or CD33, highlighting their importance in disease pathogenesis [6, 32, 41, 60, 82, 87, 101]. Single-cell profiling have revealed subpopulations of microglia which could be directly involved in AD progression [22, 25, 37, 40, 49, 50, 88]. We have recently shown that some morphologically defined microglial clusters are associated with specific protein pathologies and display sub-regional heterogeneity in the hippocampus of AD and dementia with Lewy Bodies (DLB) patients [19], with the most pronounced changes being seen in the cornu ammonis (CA) 1 of AD patients. The property of microglia to assemble in cellular aggregates around an insult is a common readout of degenerative hotspots. Indeed microglial accumulation in a rosette-like conformation around Aβ core plaques in post-mortem human AD samples or mouse models [2, 9, 16] has been particularly studied [40, 99, 103]. Yet the function of plaque-associated microglia (PaM) remains controversial, with evidence suggesting both protective and pathological roles to AD. PaM are associated with Aβ core plaque clearance [27] and formation [29, 83, 93], with lipid metabolism [28, 80] and local neuroinflammation [31, 44, 96]. Additionally, various types of microglial accumulations have been observed in numerous brain diseases such as microglia nodules or white matter-associated microglia (WAMs) in mouse models of inherited amyotrophic lateral sclerosis (ALS) [18, 89], AD [73], human multiple sclerosis (MS) [11] or viral encephalitis [90] and COVID-19 [77] brains, highlighting the complexity of microglial involvement in neurodegenerative processes. In addition to microglia, astrocytes undergo morphological and molecular changes in AD, reflecting their numerous contribution to CNS homeostasis, defence and immunity [8]. While less characterised than microglia, astrocytes may have a significant impact on the progression of AD by shifting some protective phenotypes to detrimental ones. Polarised and reactive astrocytes are found enclosing PaM, to form together an organised structure, the so-called reactive glial net (RGN) [9], and may regulate the Aβ plaque cellular surrounding architecture and spacing [30]. Moreover, infiltrating peripheral immune cells such as T cells or macrophages, which could drastically impact inflammation and microglial responses, have been described to reach Aβ plaques in AD mouse models and human post-mortem samples [86]. However, the roles of microglia, astrocytes and infiltrating immune cells in the rapid degeneration of the hippocampus still need to be further characterised.

Here we have found two morphologically distinct types of microglial accumulations in the hippocampus of late-onset AD (LOAD) patients, the typical PaM surrounding Aβ plaques, and a newly defined accumulation termed coffin-like microglia (CoM) that was specifically enriched in the CA1 pyramidal layer (PL), but much less abundant than PaM. We aimed to elucidate molecular and morphological characteristics of CA1 PaM and CoM, surrounding astrocytes and their association with infiltrating immune cells using a cohort of 52 AD, DLB and control human post-mortem samples. To this purpose, we employed spatial protein and transcriptome profiling [48, 84], high- and super-resolution confocal microscopy, chromogenic multiplexing immunohistochemistry and digital pathology.

Our analysis unveiled a comprehensive new set of features for microglia and astrocytes in AD that are specific to their neurodegenerative microenvironment and show an association with infiltrating immune cells at the level of Aβ plaques. Our study advances the characterisation of glial-immune hotspots in neurodegeneration of the hippocampus and beyond. Furthermore, our findings have the potential to inform the development of novel therapeutic strategies targeting glial and immune cells to mitigate neurodegenerative processes.

Human brain samples

Human pseudonymized paraformaldehyde (PFA)-fixed and formalin fixed paraffin embedded (FFPE) post-mortem mid-hippocampal (between the uncus and corpus geniculatum laterale) samples were obtained from the Douglas-Bell Canada Brain Bank (Douglas Mental Health University Institute, Montreal, QC, Canada) and GIE-Neuro-CEB biobank (Groupe Hospitalier Pitié-Salpêtrière, Paris, France). Their use for research was approved by the respective Ethic Panels of the aforementioned, as well as of the University of Luxembourg (ERP 16-037 and 21-009).

The neuropathological diagnosis was performed by brain bank intern neuropathologists following Braak [10], ABC staging [57] and McKeith staging criteria [53] based on Aβ plaques, NFTs and α-synuclein.

PFA-fixed hippocampal samples include non-demented age-matched controls (CTLs, n=12), DLB patients (n=4) and AD patients (n=23). FFPE hippocampal and cortical samples include controls (n= 8) and AD patients (n=19). 3 of the controls and 11 of the AD cases were available both as PFA-fixed and FFPE material. Age at death, post-mortem delay (PMD), sex, disease score and reported concomitant misfolded protein pathologies of the human subjects are listed in Table 1.

Table 1: Cohort of samples used in the study (IHC fluorescent or IHC chromogenic (Dako Omnis and Ventana Discovery Ultra) or Digital Spatial Profiling).

DIAGNOSIS	CASE	SEX	AGE (YEARS)	PMD (HOURS)	DISEASE SCORE	REPORTED CONCOMITANT MISFOLDED PROTEIN PATHOLOGIES*
CTL	1	F	86	5.75	/	/
	2	F	80	17.5	/	/
	3	F	85	12	/	/
	4	F	89	23.6	/	Some senile plaques and NFTs
	5	M	89	32.3	/	/
	6	M	80	13.0	/	Some senile plaques
	7	F	83	35.8	/	/
	8	F	95	23.8	/	/
	9	M	85	26.75	/	/
	10	F	78	16.5	/	/
	11	M	93	18	/	/
	12	M	83	16.8	/	/
	13	M	89	32.0	/	A1B1C1
	14	F	55	21.1	/	/
	15	M	72	7.48	/	/
	16	M	76	19.75	/	hypoxic-anoxic encephalopathy
	17	M	68	23.37	/	/
DLB	18	M	64	17.2	Neo-cortical	Senile plaques and NFTs
	19	F	83	17.8	Neo-cortical	Senile plaques and NFTs
	20	M	82	22.0	Neo-cortical diffuse	/
	21	F	81	8.6	Neo-cortical diffuse	/
AD	22	F	85	24.75	/	/
	23	M	80	15.5	Braak IV	/
	24	M	82	11.05	Braak IV	/
	25	M	85	31.1	A2B3C2	/
	26	F	83	25	A2B3C2 "intermediate"	/
	27	M	91	25	A2B3C2	/
	28	M	86	27.25	A2B3C2, "intermediate"	/
	29	F	96	26.5	Braak IV/V	/
	30	M	95	29.5	A2B3C2 "intermediate"	/
	31	M	83	43.25	Braak III	/
	32	F	81	23.75	A2B3C2 "intermediate"	/
	33	M	77	19.42	A2B3C2 "intermediate"	diffuse neocortical alpha-synucleopathy (DLB)
	34	M	87	10.8	N/A	AA
	35	M	87	21.8	A2B3C2	/
	36	M	93	13.87	A2B3C2 "intermediate"	/
	37	M	90	14.6	A2, B2, C2	limbic alpha-synucleopathy; CAA.
	38	M	90	26.1	A3B2C3	/
	39	F	74	27.62	A2B2C0	/
	40	F	87	17.0	A1B2C2	/
	41	M	90	32.0	A2B2C3	/
	42	M	78	18.0	Braak VI, Thal 5	AA
	43	F	86	39.0	Braak V-VI, Thal 4	LB and LN
	44	F	73	41.0	Braak V-VI, Thal 5	DLB + AA
	45	M	65	46.0	Braak V-VI, Thal 5	AA
	46	M	85	21.0	Thal 4	/
	47	M	66	30.0	Braak VI, Thal 5	AA
	48	F	84	24.0	Braak VI, Thal 5	DLB + AA
	49	F	81	41.0	Braak VI, Thal 5	/
	50	M	76	22.0	Braak VI, Thal 5	TDP-43 + DLB
	51	M	68	29.0	Braak VI, Thal 5	DLB + AA
	52	M	66	20.0	Braak VI, Thal 5	AA

AD Alzheimer’s disease, AA Amyloid angiopathy,CAA : Cerebral Amyloid Angiopathy ; CTLs age-matched controls, DLB Dementia with Lewy Bodies, LB Lewy Bodies, LN Lewy Neurites, NFTs neurofibrillary tangles, PMD post-mortem delay.

*After neuropathological examination

Immunohistochemistry (IHC)

FFPE hippocampal blocs were cut using a standard microtome into 5 µm thick sections, mounted on coated Dako FLEX IHC microscope slides (Cat# K8020, Agilent). The slides were dried in an oven at 60 °C for at least 60 min. All the slides were processed on an automated immunostainer (Dako Omnis Immunostainer, Agilent) with heat induced epitope retrieval (HIER) (EnVision^TMFLEX Target Retrieval Solution high or low pH buffer, Cat# K8004 and Cat# K8005 respectively) for 30 mins at 97 °C. Primary antibodies (anti-Iba1 Cat# 019-19741, anti-cd68 Cat# 916104, anti-STING Cat# MA5-26030, anti-C1q Cat# ab182451, anti-ITGA6 Cat# HPA012696, anti-ACADS Cat# HPA022271; antibody concentrations in Supplementary Table 1) were diluted in EnVision^TMFLEX antibody diluent (Cat# K8006). Primary antibody incubation lasted for 1h at room temperature (RT), detection based on horseradish peroxidase (HRP)/ 3, 3′-diaminobenzidine (DAB) substrate system (EnVision^TMFLEX Detection kit, Cat# K8000) and, finally, counterstained with haematoxylin (Cat# GC808). Finally, the slides were dehydrated through ethanol baths and mounted.

Multiplex chromogenic IHC

For all experiments made using Ventana Discovery Ultra automated IHC/in situ hybridization (ISH) research platform (Roche Ventana Medical Systems, Tucson, AZ, USA), 5 µm sections from FFPE blocks were mounted on Matsunami TOMO® hydrophilic adhesion glass slides (Cat# TOM-1190). Slides were dried as previously described. All reagents were included in the system as recommended by Roche Diagnostics and washing was performed in between steps with Reaction buffer (Cat# 950-300). All antibody concentrations are listed in Supplementary Table 1. For single and multiplex IHC, counterstain was done with 4 min each incubation of haematoxylin II (Cat# 790-2208) and bluing reagent (Cat# 760-2039). Consecutively, all slides were washed, dehydrated through a series of alcohols and coverslipped.

For DAB single-plex IHC, sections were first deparaffinized at 69 °C in EZ Prep solution (Cat# 950-102) for 8 min during 3 cycles. HIER was performed at 95 °C using Cell Conditioning 1 (standard CC1, Cat# 950-224) reagent for 40 min. For blocking endogenous peroxidases and proteins, slides were incubated with inhibitor ChromoMap (CM) at 37 °C for 8 min. Then, primary antibodies (anti-C3 Cat# HPA003563, anti-C4d Cat# ab36075, anti-PCSK9 Cat# MA5-32843) were added by manual application (diluted in EnVision^TMFLEX). After 60 min incubation with primary antibodies, OmniMap anti-Rb (Cat# 760-4311) or OmniMap anti-Ms HRP (Cat# 760-4310) was applied and incubated for 16 min, followed by 4 min of H₂O₂ CM, 8 min of DAB CM and 4 min of Copper CM from the Discovery CM DAB kit (Cat# 760-159).

All the multiplex protocols (two- to four-plex) followed the same initial steps of deparaffinization, HIER with CC1, and blocking with inhibitor CM, unless stated otherwise.

In the two-plex CD163/Iba1, after deparaffinization and HIER steps, one drop of Discovery inhibitor (Cat# 760-4840) was applied for 8 min. Then, anti-CD163 (Cat# 760-4437) was incubated for 60 min and developed by OmniMap anti-Ms HRP during 16 min, followed by 4 min of Discovery Purple and 32 min of H₂O₂ Purple from the Discovery Purple Kit (Cat# 760-229). Heat mediated (100 °C, 24 min) antibody denaturation was performed using Cell Conditioning 2 (CC2) reagent (Cat# 950-223) prior to each cycle of sequential staining in every panel to denature the primary antibody-HRP complex and prevent subsequent chromophores of binding to any residual unbound HRP-conjugated primary antibody. Then, anti-Iba1 (Cat# 019-19741) was added for 48 min. The signal was amplified with OmniMap anti-Rb HRP for 16 min followed by a Discovery Teal HRP chromophore (Cat# 760-247) consisting of an incubation of 4 min of Teal HRP Substrate, 32 min of Teal HRP H₂O₂and 16 min of Teal HRP Activator.

The three-plex experiments were conducted as follows: for the CD163/Iba1/4G8 combination, anti-CD163 was incubated as before and stained with Discovery CM DAB as described in single-plex protocol. After denaturation, anti-Iba1 was applied during 48 min, followed by OmniMap anti-Rb HRP for 16 min and then Discovery Purple Kit. For the last step, anti-4G8 (Cat# 800712) was incubated 1 h and amplified with OmniMap anti-Ms HRP for 16 min, followed by a Discovery Teal HRP.

For the Iba1/4G8/GFAP and PS396/4G8/GFAP combinations, anti-Iba1 and anti-PS396 (Cat# 44-752G) were incubated respectively 1 h 32 min and 1 h, followed by OmniMap anti-Rb HRP incubation (20 min) and DAB staining as in the single-plex IHC. Subsequently, anti-4G8 was added for 60 min followed by 12 min incubation with UltraMap anti-mouse Alk Phos (AP) (Cat# 760-4312) for 12 min. Then DISCOVERY Yellow kit (RUO, Cat# 760-239) consisting of 4 min of Yellow buffer and 44 min of Disco Yellow was applied. Finally, anti-GFAP (Cat# 760-4345) was applied for 40 min, followed by OmniMap anti-Rb HRP for 16 min and Discovery Teal HRP.

The four-plex IHC, Iba1/AT8/4G8/pSer396 and Iba1/CD3/4G8/CD8 followed a similar protocol. Briefly, anti-Iba1 was applied for 1 h 32 min followed by OmniMap anti-Rb HRP incubation (20 min) and Discovery CM DAB kit. Anti-AT8 (Cat# MN1020) and anti-CD3 (Cat# 790-4341) were incubated respectively for 60 or 32 min, followed by respectively OmniMap anti-Ms HRP or OmniMap anti-Rb HRP for 16 min and coupled with Discovery Purple kit. Then, anti-4G8 yellow staining was done as in the previous three-plex. Finally, anti-pSer396 (Cat# 44-752G) or anti-CD8 (Cat# 790-4460) was added for respectively 60 or 40 min, amplified with OmniMap anti-Rb HRP for 16 min and coupled to the Discovery Teal HRP.

Fluorescent IHC

PFA-fixed samples were cut and immunostained as previously described [19, 65, 74]. Briefly, PFA-fixed samples were washed in phosphate-buffered saline (PBS), cryo-preserved in 30% sucrose and cut into 80 µm thick sections on a sliding freezing microtome (LeicaSM2010R). After overnight ultraviolet (UV) irradiation for autofluorescence reduction the sections were permeabilized through 30 min incubation with 0.5% Triton-X 100 prior the blocking step with 2% horse serum for 2 h. The sections were incubated with primary antibodies in blocking solution with concentrations in Supplementary Table 1 (anti-Aβ Cat# 800712, anti-GFAP Cat# 173004, anti-Iba1 Cat# 019-19741, anti-Iba1 Cat# LS-B2402, anti-pSyn Cat# NA, anti-pTau Cat# MN1020, anti- NF-κB Cat# ab86299, anti-pMLKL Cat# OASG07777, anti-erbB4 Cat# HPA012016, anti-C3 Cat# HPA003563, anti-ITGA6 Cat# HPA012696, anti-SMURF2 Cat# HPA071508, anti-ACADS Cat# HPA022271), for 72 h at 4°C. Afterwards slices were washed with PBS and incubated with secondary antibodies (Donkey anti-mouse Alexa Fluor 488 Cat# 715-545-150, Donkey anti-goat Alexa Fluor 488 Cat# 705-545-147, Donkey anti-guinea pig Alexa Fluor 488 Cat# 706-545-148, Donkey anti-rabbit Alexa Fluor 555 Cat# A-31572, Donkey anti-guinea pig Alexa Fluor 647 Cat# 706-605-148, Donkey anti-mouse Alexa Fluor 647 Cat# 715-605-150, Donkey anti-goat Alexa Fluor 647 Cat# 705-605-003, Goat anti-rabbit Atto 647N Cat# 40839) for 2 h at RT. After 3 washing steps with PBS, some sections were counterstained with DRAQ7^TM far-red fluorescent DNA dye (Cat# 7406, 20 min; RT) to label nuclei or with 0.2 μM Thiazine red (TR) solution (Sigma Chemicals, St. Louis, MO, USA) solution for 20 min at RT to stain Aβ plaques and tau fibrils. Samples were then washed two times for 10 min in PB prior to mounting on glass slides (Superfrost Microscope slides) using ProLong Gold Antifade reagent (Cat# P36930).

Bright-field Microscopy

Bright-field images of chromogenic stained FFPE sections were acquired at 5,10, 20 and 40X objectives of a Leica bright-field DM2000 LED microscope and a Leica DMC2900 camera (Leica Microsystems) or by a high-throughput bright-field slide scanner (IntelliSite Ultra Fast Scanner, Philips).

Confocal and stimulated emission depletion (STED) microscopy

3D z-stack confocal acquisitions were obtained by LSM710 and LSM800 confocal microscopes with 20X air (Zeiss Plan-APOCHROMAT 20x/0.8 420650-9902) and 40X oil (Zeiss EC Plan-NEOFLUAR 40x/1.3 Oil DIC 420462-9900) objectives.

3D STED z-stack acquisitions were obtained with a Leica DMi8 with TCS SP8 microscope equipped with 100X oil STED objective (Leica HC PL APO 100x/1.40 OIL STED white ∞0.17/OFN25/D) and 775nm STED depletion laser. STED images were deconvolved with Huygens Professional software (Scientific Volume Imaging). Tile scans were stitched using the LAS X software (Leica Microsystems), Zeiss microscope software ZEN or Imaris Stitcher (Supplementary Table 2). 3D stacks were visualized with Imaris (9.6.0 and 9.8.0) software after each channel underwent intensity normalization and background subtraction to remove noise.

Digital Pathology

All chromogenic IHC image analyses were performed using HALO® image analysis platform (Indica Labs, version 3.6). Hippocampi were manually divided into 4 sub-regions (CA4, dentate gyrus (DG), CA3 and CA2/CA1) and their areas were measured. CD68 quantification was performed using the HALO® area quantification module, which measures stain-positive areas. CD8+ Tcells were quantified manually and differentiated between cells found in blood vessels (BV) and parenchyma. Percentages of stained area or cell count (cells / mm2) were compared using t-tests with adjustment for multiple comparisons (using Holm’s method). To quantify the number of PaM and PaM astrocytes, but also CD163+ and CD163+Iba1+ double-positive cells associated with Aβ plaques and assess their relationship to the area of the plaques in the CA1 subfield of the hippocampus, we first manually delineated the surface of each plaque and then, manually counted the number and type of cells attached (color based). We then compared the mean number of cells attached to 1000 µm² of amyloid area using t-tests with adjustment for multiple comparisons (Holm’s method). Then we described the Pearson correlation between cell type and plaque area using ordinary least squares linear regression.

Volumetric quantification and nuclei count

Volume and nuclei count of 61 microglia accumulations were assessed in high-resolution confocal acquisitions (40x) of Iba1 and DRAQ7^TM immunostained hippocampal sections from age-matched CTLs (n=1), AD (n=8) and DLB (n=4) subjects. Using the surface tool from the Imaris (9.8.0) software, microglia accumulations were 3D reconstructed (surface detail parameter of 400µm) and volume was extracted. Subsequently, nuclei of each accumulation were manually marked and counted with the spots tool.

Deep Spatial Profiling (DSP)

Sample preparation for DSP

Post-mortem FFPE hippocampus blocs from three AD patients (cases #49, #50 and #52; Table 1) were cut into 5 µm thick sections on a microtome. Sections were mounted on microscopy slides, without coverslip (Thermo Scientific Superfrost Plus, J1800AMNZ), to produce three slides with each containing one section of all AD patients (three sections per slide) that were cut at different depths to avoid overlap of identical microglia accumulations. Finally, slides were dried overnight at 37 °C in an oven. Two slides were used for spatial transcriptomic analysis and one slide for spatial proteomic analysis.

Spatial proteomics

GeoMx DSP technology (NanoString Technologies) was used to perform cell-type enriched spatial proteomic analysis. Areas of illumination (AOIs) were manually drawn on the GeoMx DSP analysis suite (Version 2.5.1.145) software based on fluorescent antibody morphology markers including anti-Iba1 (Cat# 48934), anti-GFAP (Cat# NBP2-33184DL594), anti-Aβ (Cat# NBP2-13075AF532) and SYTO^TM 13 green fluorescent nucleic acid stain (Cat# S7575). PaM and CoM were distinguished through, respectively, presence or absence of Aβ plaques and Iba1-positive area, as well as surrounding areas were segmented. In total 24 AOIs for segmentation were selected: 7 PaM-Iba1, 7 PaM-Surrounding, 5 CoM-Iba1 and 5 CoM-Surrounding. The Human Protein Next Generation Sequencing (NGS) panel probe mix was used and contained in total 147 targets from the following modules: human protein core for NGS, immune cell typing, immune activation status, 10 drug target, pan-tumour, cell death, MAPK signalling, P13K/AKT signalling, myeloid, autophagy, neural cell typing, AD pathology, AD pathology extended, Parkinson’s disease pathology and glial cell subtyping.

Upon AOI specific UV illumination, indexing oligonucleotide tags from the probe mix were released, collected in a 96-microwell plate and analysed on an nCounter® platform. FASTQ files for Nanostring GeoMx were aggregated into count matrices based on Unique Molecular Identifier (UMI) and molecular target tag sequences. Single probe genes were reported as the deduplicated count value. The digital counts were normalized to negative probes (Ms IgG1, Rb IgG, Ms IgG2a).

Spatial transcriptomics

GeoMx DSP technology (NanoString Technologies) was used to perform cell-type enriched spatial transcriptomic analysis. AOIs were selected based on the identical morphology markers as for the spatial proteomic analysis. AOIs from three AD patients (in duplicate but at different depths levels) were localized in the pyramidal layer of the hippocampus. PaM and CoM AOIs were identified as previously described and Iba1-positive areas were segmented as well as GFAP-positive area. For rod-shaped microglia (Rod) AOIs exclusively Iba1-positive area was segmented. In total 38 AOIs for segmentation were selected: 10 PaM-Iba1 AOIs, 10 PaM-GFAP AOIs, 7 CoM-Iba1, 7 CoM-GFAP and 4 ROD-Iba1 AOIs. The probes in overlapping areas of Iba1 and GFAP staining were included in Iba1 segmentation. The probe mix of 18,677 ISH probes conjugated to UV-photocleavable indexing oligonucleotide tags (Transcriptomics Probekit (v1.0) Human NGS Whole Transcriptome Atlas RNA) were added to the slide and indexing oligo sequences were released and collected in all AOIs and counted.

We removed targets with expression lower than 5% segments above limit of quantification (LOQ). We then scaled the dataset to the geometric mean of nuclei and finally performed quartile (Q) 3 normalization, creating a dataset with 15,518 transcripts.

Over-representation analysis (ORA)

We performed ORA using the R package clusterProfiler [102] for differentially expressed genes (DEGs) having a log-fold change lower than -0.5 or greater than 0.5 and associated p value < 0.1 after curating the database for all microglia-associated or astrocytes-associated transcripts validated in the literature and in online databases such as BrainRNAseq (https://brainrnaseq.org) or astrocyteRNAseq (http://astrocyternaseq.org) for astrocytes. We employed the following databases: Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Wikipathways (WP).

Quantification and statistical analysis

For the volume quantification and nuclei count analysis (61 microglia accumulations) a Kruskal-Wallis test by ranks was performed using GraphPad Prism software (Version 9.4.1). In the spatial proteomic analyses of 147 target proteins in 24 AOIs differentially expressed proteins (DEPs) were detected by applying two-sided unpaired t-tests. In the spatial transcriptomic analyses of target transcripts in 38 AOIs, DEGs were detected by applying unpaired t-tests assuming a normal distribution of the data. All p values were adjusted for multiple comparisons using the Benjamini-Hochberg procedure with false discovery rate (FDR) of 0.1. Heatmaps and volcano plots were generated in R (version 4.2.1.) using the pheatmap (version 1.0.12) and ggplot (version 3.3.6) packages. In this manuscript and supplementary files, * indicates P < 0.05, ** P < 0.01 and *** P<0.001. In Supplementary Excel 1 for multiple groups of comparisons, p.adj denotes p values adjusted by the Benjamini-Hochberg procedure for one single group of comparisons, while p.adj2 is adjusted for the entirety of comparisons.

Microglia regroup into morphologically distinct cell aggregates in AD hippocampi.

To elucidate the alterations of microglia within the hippocampus of AD patients, we performed IHC analysis of the microglial morphological marker, ionized calcium-binding adaptor molecule 1 (Iba1),in a cohort of CTLs and AD post-mortem FFPE samples. We identified two distinct types of large Iba1-positive cellular accumulations. The most common were the typical Aβ plaque-associated microglia (PaM), characterized by a high number of microglia clustered around an Aβ core plaque in a rosette-like conformation. Less frequently encountered, but consistently present in our sample collection, were clusters of microglia with a polarised shape reminiscent of a 'soma' and 'tail' configuration, observed predominantly in the pyramidal layer (PL) of the CA1 and CA2 regions of the hippocampus (Fig. 1A-B). Across serial sections of hippocampi from 16 AD cases and 5 age-matched CTLs, we observed a ratio of approximately 1 of this type of accumulation to 11 PaM in the CA of AD patients, whereas it was absent in CTL samples, as well as in the prefrontal, temporal and entorhinal cortices of both CTL and AD patients.

To further characterise the three-dimensional (3D) morphological features and differences from PaM, we immunostained PFA-fixed 80 µm hippocampal sections from AD patients for Iba1 and 4G8 (Aβ). Using confocal and STED microscopy, we confirmed the enrichment of this peculiar type of Iba1- accumulation in the CA1 and CA2 PL of AD hippocampi (Fig. 1C) and showed that they were spatially separated from 4G8-positive Aβ plaques (Fig. 1D-G, Supplementary Fig. 1A-B). High and super-resolution microscopy revealed new features of their complexity with Iba1-cells showing tight boundaries and an encapsulating cellular organisation that was clearly distinct from PaM [9, 16] with a size and consistent orientation resembling that of pyramidal neurons (Supplementary Fig. 1, Supplementary Movie 1; Supplementary Movie 2). Co-staining with DRAQ7™ often showed nuclei that appeared to be engulfed by Iba1-positive cells (Supplementary Movie 1, Supplementary Fig. 2). These microglia collectively formed a tight shell around as yet-unidentified structures, resembling the glial embrace and coffin formation described in early twentieth-century literature [2, 24, 38], that we hereafter referred to as “coffin-like microglia” or CoM (Fig. 1A, C, E, G; Supplementary Fig. 1B, D).

Our high-resolution 3D analysis allowed us to distinguish between two types of CoM. We noticed some CoM formations were predominantly composed of bipolar or rod-shaped microglia, resulting in a thin and elongated structure in 3D, often traversing the PL, while only partially enveloping surrounding cells. Unlike the first CoM described, the other type did not seem to enclose any other structure. We termed them CoM-Rod (Fig. 1H, Supplementary Fig. 3). In addition, we often observed smaller groups of Iba1-positive cells 'clumped' together, located between other brain cells (Supplementary Fig. 3). Our previous findings indicated that morphological alterations of individual microglia in DLB resembled to those observed in AD, but with less severity [19]. Consistent with these observations, we detected PaM, CoM, CoM-Rod, and clumped microglia to a lesser extent in the hippocampi of DLB patients (Supplementary Fig. 1C-D).

We quantified CA1/CA2 PaM and CoM number of cells and their total volume from 3D confocal stacks (40x). We did not observe CoM in age-matched control samples (n = 8), however, we found few PaM and CoM-Rod. Using Imaris software, we quantified volumes and number of nuclei contained in 61 3D z-stack images of PaM, CoM and CoM-Rod across age-matched control (n = 1), AD (n = 8) and DLB patients (n = 4) samples (Fig. 1H). PaM presented a mean volume of 19,744µm³ with an average of 20 nuclei, CoM had a mean volume of 15,776µm³ and an average of 17 nuclei, and CoM-Rod a mean volume of 8,870µm³ and an average of 9 nuclei (Fig. 1H). Using the Kruskal-Wallis test by ranks, we showed that CoM-Rod are significantly smaller in volume (P < 0.05, P < 0.01 respectively) and in nuclei count (P < 0.01) than CoM and PaM. Even though PaM and CoM are very different in morphology, their volume and nuclei count did not present significant differences.

We then investigated the association of a typical marker of microglia upregulated in disease, cluster of differentiation 68 (CD68) [7, 27, 68]. We first quantified CD68 expression in hippocampal subfields of CTL and AD FFPE samples (n = 7 CTL, 8 AD). CD68 was expressed in microglia-like cells in CTL and AD in all brain regions examined, slightly increased in all subfields but more drastically in the CA1 AD (P < 0.05). We found CD68 associated with PaM and CoM-like structures in the CA hippocampus, showing a shared feature of PaM and CoM (Fig. 1I).

PaM and CoM exhibit distinct pathological and astrocytic microenvironments.

To elucidate the relationship of PaM and CoM with surrounding cells, tau and α-synuclein pathologies, we investigated the characteristics of their pathological microenvironment. Given the distinct association of PaM and CoM with Aβ plaques, we further investigated their interactions with pTau NFTs and pSyn aggregates in AD [19, 92]. Using high-resolution confocal imaging of immunostained sections for Iba1 and AT8 (pTau), we found that CoM can fully encompass NFT-bearing neurons, enveloping the entire 'flame' shape of the tangle and spreading to surrounding neuronal cells (Fig. 2A-B, Supplementary Movie 3). Using thiazine red (TR), which binds β-pleated sheet structures, we also observed CoM containing TR-positive structures resembling NFTs (Supplementary Fig. 4B, Supplementary Movie 4). Similarly, we found some cells harboring intracellular granular pSyn inclusions enveloped by CoM (Fig. 2C-D, Supplementary Movie 5). Notably, some CoM lacked AT8 tangles (Supplementary Fig. 4A) or pSyn aggregates.

We next examined the distribution of the P-Ser396 pTau (PS396), a preferred site of GSK-3 β phosphorylation activated by phospho-AKT1 (S473) [78], in combination with the S202 and S205 phosphorylated forms of tau labelled by AT8, 4G8 and Iba1 staining in IHC multiplex chromogenic experiments (n = 4 AD). We found relative heterogeneity of PS396 and AT8 tangles and neurites across samples, with some samples showing a distinct ratio of PS396 to AT8 loading (Fig. 2E-F). We observed single stained neurons, AT8 + or PS396+, and double stained AT8 + PS396+. The plaque microenvironment defined by 4G8 staining and Iba1 PaM showed a severe tau pathology, with both in AT8 and PS396 labelled swollen structures and often double-positive (Fig. 2E-F). Groups of microglia reminiscent of CoM were also found surrounding neurons with double positive tangles (Fig. 2F). To understand the compartmentalisation of neurodegenerative processes around Aβ plaques, we used multiplex chromogenic labelling for PS396 Tau, 4G8 and glial fibrillary acidic protein (GFAP), to label PaM astrocytes that were shown to form nets [9]. We found that astrocytes frequently encapsulate PS396 + structures and Aβ plaques (Fig. 2G).

We then investigated in more detail the relationship of PaM and CoM with surrounding astrocytes, whose morphological changes may indicate a specific interplay with microglia. Using chromogenic multiplex IHC, for Iba1, GFAP and 4G8 (n = 5 CTL, 8 AD), we quantified the number of PaM and PaM-associated astrocytes (PaM astrocytes), those polarised around PaM and forming a net, associated with Aβ plaques (n = 391) in the CA1 and measured the corresponding plaque area (Fig. 3A-C). As expected, more PaM than PaM astrocytes were attached to Aβ plaques per area (Fig. 3B). We found a positive correlation between CA1 plaque area, the number of PaM (r = 0.398, ***) and the number of astrocytes forming a net (r = 0.37, ***) (Fig. 3C) with a similar trend as in the cortex [9]. In total, 26.1% of Aβ plaques were encapsulated by both PaM and PaM astrocytes, 22% of Aβ plaques smaller than 2500 µm², 53.3% of Aβ plaques larger than 2500 µm². With this approach, there was no evidence of CoM encapsulation by astrocytes. Using 3D confocal microscopy, we compared the distribution and morphology of the astrocytes surrounding PaM or CoM. Around core Aβ plaques (4G8), hippocampal PaM (Iba1) were often surrounded by an outer sphere of hypertrophic and polarised astrocytes (GFAP, PaM astrocytes), as previously observed in AD cortical brain samples (Fig. 3D), but also in APP mouse models [9]. However, we found that astrocytes surrounding CoM ( CoM astrocytes) mainly exhibited a dysmorphic phenotype, with thin and irregular branches, lacking polarisation towards the CoM and showing lower reactivity, here assessed by GFAP intensity (Fig. 3E, Supplementary Fig. 4B). Some GFAP processes were found invading CoM boundaries suggesting a possible interaction at subcellular level different from that around Aβ plaques (Supplementary Movie 4).

Digital spatial profiling of 147 proteins highlights core differences between PaM and CoM and their microenvironment.

To delve deeper into the molecular signatures of PaM and CoM in relation to their surrounding microenvironment in AD, we used the Nanostring GeoMx Digital Spatial Profiler (DSP) platform. We quantified 147 proteins in spatially defined CoM (n = 5) and PaM (n = 7) Iba1-positive areas and their close surrounding in FFPE hippocampal sections from three AD patients (cases #49, #50 and #52; Table 1). Utilising fluorescent antibodies for microglia (Iba1), astrocytes (GFAP), Aβ-plaques (MOAB-2) and nucleic acid stain (SYTO™ 13), we accurately localised and identified PaM with astrocyte net and CoM. By manually delineating regions of interest for PaM and CoM in the hippocampal PL from the CA2 and CA1, we selected two AOIs: the Iba1-positive area and its immediate surrounding (Fig. 4A). After negative probes normalisation, only a limited number of DEPs reached significance FDR > 0.1, |Log₂ fold change (FC)|>0.5, p_adj<0.05) between PaM and CoM and between their respective surroundings (Fig. 4B, Supplementary Excel 1). Her2 (receptor tyrosine-protein kinase erbB2), complement component 4B (C4B), and protein kinase B (Phospho-AKT1, S473) were enriched in PaM compared to CoM while stimulator of interferon genes (STING) exhibited higher levels in CoM. Granzyme A (GZMA), progesterone receptor (PR), and p53 were enriched in vicinity of PaM vs. CoM. Despite not reaching statistical significance, several proteins indicated potential differences in cellular signatures or microenvironments. Purinergic receptor P2RY12, Iba1, and microglia/immune cells associated proteins such as arginase 1 (ARG1) or lymphocyte activating 3 (LAG3) were elevated in CoM compared to PaM. Amyloid peptides, amyloid precursor protein (APP), GFAP, CD163, CD44 and apolipoprotein E (ApoE) were found at higher levels in both PaM and PaM surroundings in coherence with our data and literature [58, 62]. We further validated some of these findings with immunohistochemistry (IHC), in FFPE and/or in PFA AD hippocampal sections. We confirmed the enrichment of STING in CoM, as well as C4d, a cofactor of C4B, and ErbB4, a co-receptor of ErbB2 [43] in PaM and PaM surroundings (Fig. 4C-E). Specifically, STING expression was elevated in AD hippocampal samples (8 AD, 7 CTL, FFPE), often associated with blood vessels and microglia-like cells in the white matter of the stratum oriens, CA3, and particularly the PL of CA1 (Fig. 4C). Often microglia aggregated around pyramidal neurons of the CA1 were STING immunoreactive. C4d staining was enriched around Aβ plaques in the hippocampus (CA3, CA1 and DG), subiculum, entorhinal and temporal cortex in a set of our samples (3 AD, 3 CTL, FFPE), validating previous observations [85] (Fig. 4D). Our 3D confocal analysis revealed the presence of ErbB4-positive PaM encapsulated by GFAP-positive astrocytes in PFA-fixed hippocampal sections (Fig. 4E). These observations highlight distinct functional states and pathological microenvironments between PaM and CoM.

Digital spatial transcriptomic profiling reveals distinct and complex molecular and metabolic states of PaM vs. CoM and surrounding astrocytes.

To unravel the molecular profiles and functional states of PaM and CoM and their respective immediate surrounding cells, we used the GeoMx DSP transcriptomic platform to quantify gene expression (18,677 RNA targets) in Iba1-positive PaM and CoM, and their respective surrounding GFAP-positive astrocytes (Fig. 5A). In addition, we also profiled some Rod, defined by their elongated morphology, and sometimes clustered in CoM-Rod (Supplementary Fig. 5A). We assessed a total of 38 AOIs across two consecutive sections from three AD patient samples (cases #49, #50 and #52): 10 PaM-Iba1, 10 PaM-GFAP, 7 CoM-Iba1, 7 CoM-GFAP, and 4 Rod-Iba1 AOIs. Under UV illumination, the indexing oligonucleotide tags were collected from the probe mix and counted individually from each Iba1-positive and, their respective, GFAP-positive AOI. Briefly, all AOIs passed sequencing quality control and targets with expression in less than 5% of the segments above the LOQ were removed. The target count matrix was scaled to the geometric mean of the nuclei and normalised by the upper quartile (Q3) normalisation. A final dataset of 15,518 genes was analysed.

Comparative analysis of the 500 most highly expressed genes in each microglial and astrocyte subgroup revealed overlaps and unique gene sets. We found an overlap of 197 genes (20.6% of total genes) between all the three groups of microglia, and 290 genes (40.8%) between the two groups of astrocytes (Fig. 5B). Approximately 20% of genes were exclusive to one specific group for microglia and 30% for astrocytes. Among the highly expressed genes in all microglial subgroups were allograft inflammatory factor 1 (AIF-1), CD74, or colony-stimulating factor-1 receptor (CSF-1R).

In contrast, astrocytic subgroups exhibited expression of astrocyte-specific markers such as GFAP, aldolase C fructose-bisphosphate (ALDOC), and ezrin (EZR). We then analysed the DEGs between the microglia or astrocyte subgroups using unpaired t-test and multiple comparison adjustment (Benjamini-Hochberg with FDR of 0.1). We identified 1,238 DEGs in our microglia dataset and 895 DEGs in our astrocyte dataset. Unsupervised clustering resulted in a clear separation of all AOIs into morphologically predefined groups for the microglia and astrocyte datasets, as shown in the heatmaps (Fig. 5C). DEGs were displayed in volcano plots for all comparisons (Fig. 5D, Supplementary Fig. 5B-C for Rod) and the top 30 DEGs identified in heatmaps (most enriched and lowest p-value) (Fig. 5E, G, Supplementary Fig. 5D-E). When comparing PaM to CoM transcripts, we found RAB27B, RHOQ, PCED1B, TMED8, HCAR1, CLVS1, GOLPH3, PTPRT, DCHS1, TNFAIP1, FOXO1, OXCT2, and HMCES among the most enriched and statistically significant genes for PaM; while in CoM they were C9orf131, TBXT, CASP2, AICDA, E2F6, ATP5F1D, MAP2K5, GIPC3, PPP1R9A, FBXL15, ACADS, ZMAT2, GSDME DUS3L, GDF1, and BLOC1S3. Rod-shaped microglia also showed numerous DEGs compared to both CoM and PAM. TAAR1, BCKDHA, CTNNAL1, EDIL3, RYR1, CALML4, RAX2, SLC20A2 and SERPINB6 were among the top 20 most DEGs in both comparisons (Supplementary Fig. 5B-C, Supplementary Excel 1).

We then performed an over-representation analysis (ORA) for PaM and CoM DEGs. The Gene Ontology (GO) pathways over-represented in PaM revealed phosphorylation and lipid degradation activities, antioxidant and glutathione transferase and tyrosine kinase activities. Consistent with this, the ORA based on the Kyoto Encyclopedia of Genes and Genomes (KEGG) showed associations with sphingolipid signaling pathways, insulin resistance, ErbB and Wnt signaling pathways or necroptosis (Supplementary Excel 2) (Fig. 5H). The top five GO pathways for CoM were associated with ubiquitin-like protein transferase, purinergic nucleotide receptor, oxidoreductase cyclin-dependent kinase activities (Fig. 5H). ORA KEGG and Wikipathways (WP) suggested associations with transforming growth factor (TGF)-β and NF-κB signaling, among others (Supplementary Excel 2).

Next, we analysed DEGs in PaM astrocytes vs. CoM astrocytes. PaM astrocytes were associated with distinct signatures, and we found GOLGA8F, PCSK9, HOXD13, PIWIL1, PIK3C3, FAM71E2, FLG2, GBA2, SLC10A5, ACE, FBXO10, ARHGAP29, CES1, TMEM273, and GALNT7 among the most enriched genes (Fig. 5F-G, Supplementary Excel 1). We found SERPINA5, TUBB2A, FAM171A1, PROB1, GP1BA, TMEM37, FNDC4, JUNB, SCN5A, TSC22D4, GFPT1, BASP1, and UBE2W among the most enriched and statistically significant genes for CoM astrocytes. We performed ORA on PaM astrocytes and CoM astrocytes DEGs. ORA GO of DEGs PaM astrocytes showed enrichment for fibroblast growth factor receptor signalling, response to TGF-β, glucose transmembrane transport, positive regulation of apoptotic process and amyloid beta clearance (Supplementary Excel 2). ORA KEGG and WP indicated activities associated with PI3K-Akt signalling pathway, cholesterol metabolism or cell adhesion molecules, fatty acid and lipoprotein transport or complement system. ORA GO highlighted the association of DEGs CoM astrocytes with metabolite and energy generation (OXPHOS), cytokine mediated pathways but also with adaptive immune response, JAK-STAT signalling and cellular response to misfolded proteins (Fig. 5H, Supplementary Excel 2). Our DSP analysis revealed specific cellular and molecular identities of PaM and CoM, and of their respective surrounding astrocytes.

Markers of the molecular signatures of PaM, CoM and surrounding astrocytes in the hippocampus of AD patients.

To validate the molecular signatures identified in PaM and PaM astrocytes by our DSP analysis, we performed IHC on selected proteins that embodied an enriched pathway in FFPE or PFA samples. First, we examined the complement component 1q (C1q) (n = 5 AD, 5 CTL) and complement component 3 (C3) (n = 3 AD, 2 CTL) association with PaM and the PaM environment or PaM astrocytes. In AD, C1q was strongly distributed around plaques, and on some hippocampal neuronal cell bodies and neurites in the CA3-CA1 subfields, but also in the DG (Fig. 6A) and entorhinal cortex (Supplementary Fig. 6A), and occasionally diffuse in areas reminiscent of astrocyte domains. C1q-positive hippocampal neurons were also found in CTL (Supplementary Fig. 6A). Similarly, C3 staining exhibited lower overall expression, primarily labeling the core and corona of Aβ plaques in AD (Fig. 6B, Supplementary Fig. 6B). Using high-resolution 3D confocal imaging, we observed C3 expressed within GFAP-positive PaM astrocytes territories, in cell bodies but also along processes (Fig. 6C). We then analysed the distribution of Proprotein convertase subtilisin/kexin type 9 (PCSK9) (n = 3 AD, 2 CTL) and Integrin subunit alpha 6 (ITGA6, also known as CD49f [5]) (n = 5 AD, 5 CTL), associated with the PaM astrocytes and PI3K-Akt pathways in our previous analysis. In FFPE sections, we observed a PCSK9 expression in neurons both in CTL and AD ( Supplementary Fig. 6), however in AD samples, PCSK9 staining was also associated with plaque microenvironments and with cells resembling PaM astrocytes in hippocampus and entorhinal cortex (Fig. 6D-E, Supplementary Fig. 6). ITGA6 was predominantly distributed along blood vessels in CTL, while in AD, expression around blood vessels was weaker and occasionally observed near plaques, apparently associated with PaM astrocytes. We confirmed a low expression of ITGA6 in the cell bodies and processes of PaM astrocytes with confocal microscopy (Fig. 6F). We then investigated the association of the PaM microenvironment with necroptosis identified through pathway analysis. We found, in bright-field and 3D confocal images, a clear enrichment of phosphorylated mixed lineage kinase domain-like protein (pMLKL), part of the necrosome complex, in PaM vicinity but no staining in CoM (Fig. 6G). PMLKL was frequently concentrated in vacuole-like accumulations within the cytoplasm of numerous pyramidal neurons in AD samples and, to a lesser extent, in CTL as previously described [39].

DSP analysis suggested that CoM was involved in different biological processes than PaM. To further elucidate these processes, we performed co-immunostaining against Iba1, GFAP and NF-κB -p65 in PFA sections revealing strong expression and translocation of NF-κB -p65 into CoM nuclei by confocal microscopy (Fig. 6H). The staining was not exclusive to CoM, but also detected in the surrounding microenvironment, without being predominantly expressed in astrocytes. In contrast, PaM and Pam astrocytes displayed heterogeneous NF-κB -p65 staining patterns (Fig. 6I). ORA analysis revealed strong ubiquitin ligase activity in CoM. Confocal microscopy analysis of E3 ubiquitin-protein ligase SMURF2 distribution in AD PFA-fixed samples revealed expression in CoM, although not significantly enriched compared to the surrounding microenvironment (Fig. 6J). Acyl-CoA dehydrogenase (ACADS) is associated with fatty acid oxidation and high metabolic activity. In CTL samples, ACADS was expressed in astrocytic cells in the temporal cortex, CA4 hippocampus and CA3-CA1 PL. In AD, astrocytes retained ACADS expression, but so did some microglia surrounding pyramidal neurons. 3D confocal analysis confirmed the co-distribution of ACADS in the CoM (Fig. 6K-L).

Macrophage and T cell association with PaM and CoM.

To determine whether infiltrating immune cells contribute to the PaM or CoM molecular markers and composition, we next examined the distribution of T cells and CNS-associated macrophages in CTL and AD hippocampal samples. Indeed CoM exhibited a cellular organisation reminiscent of nodules observed in various brain infections, often containing T cells [77, 90]. First, we quantified the number of CD8 + T cells, which have previously been detected near Aβ plaques in AD brain samples, in CTL and AD hippocampi (4CTL, 6 AD). CD8 + T cells were mainly found in blood vessels (BV) and less frequently in hippocampal parenchyma (Fig. 7A). CD8 + T cells were slightly increased in AD without reaching significance (Fig. 7B). To further characterise the properties and distribution of CD8 + T cells, we used multiplex chromogenic IHC to stain CD3 in combination with CD8, together with 4G8 for Aβ plaques and Iba1 for microglia in some CTL and AD hippocampal samples (CTL n = 3; AD n = 6). With this approach, we detected only single positive cells, mainly CD8 + and sometimes CD3+ (Fig. 7C-D). In CTL hippocampus, CD8 + cells were strongly localised in the BV, with occasional presence in the parenchyma, close to microglia. In AD, we again observed a slight increase in parenchymal CD8 + cells, sometimes infiltrating in hotspots, but with very little to no association with PaM, Aβ plaques or CoM (Fig. 7D). Similar observations were made in the entorhinal and temporal cortices of AD samples (Supplementary Fig. 7A).

We next examined the presence of macrophages as DSP revealed shared transcripts between PaM and peripheral immune cells. As CD163 was one of the proteins enriched in PaM and the PaM surroundings, we investigated its distribution using two multiplex chromogenic IHC strategies. When we coupled Iba1 staining with CD163, we observed, in CTL samples (n = 3), that CD163 + perivascular cells were regularly distributed along the BV, while Iba1 + cells populated the parenchyma. Few Iba1 + CD163 + cells were detected lining the capillaries (Fig. 7E). In contrast, AD samples (n = 4) exhibited a notable increase in CD163 + cells within the parenchyma, often regrouped among PaM-like accumulations with several CD163 + cells being double positive for Iba1(Fig. 7E). CD163 + Iba1 + were abundant in the white matter of the stratum oriens. To further understand their relationship with Aβ plaques, we combined CD163, Iba1 and 4G8 staining on CTL and AD samples (5 CTL, 8 AD). We found a recurrent co-distribution of CD163+, Iba1 + and Iba1 + CD163 + around Aβ plaques in the hippocampus (Fig. 7F), entorhinal and temporal cortices (Supplementary Fig. 7B). Using digital pathology, we have quantified the association of Iba1+, CD163+, and CD163 + Iba1 + with Aβ plaques and their respective areas in the CA1 of AD cases. The number of Iba1 + PaM associated with plaque area was significantly higher than CD163 + and CD163 + Iba1 + which were the less frequent. We found a positive correlation between CA1 plaque size and the number of PaM (r = 0.273, ****) but low positive correlation for CD163+ (r = 0.128, **) and CD163 + Iba1+ (r = 0.111, *).

Our investigation provides a comprehensive dissection of the molecular identities underlying microglia-encapsulated neurodegenerative microenvironments, particularly evident in the aggregation patterns observed in the hippocampi of AD patients. Using a combination of spatial protein and transcriptomic profiling, brightfield chromogenic multiplex IHC and 3D confocal and STED microscopy, our findings highlight the multiple identities of microglia and astrocytes in AD and their potential impact on the deterioration of the hippocampus, and the brain.

We describe the distinct structural and functional attributes of PaM and their surrounding cells, the astrocytes, as well as a newly identified type of microglia accumulation termed CoM, enriched in AD CA1. While CoM resemble microglial nodules found in multiple sclerosis or brain infections [11, 18, 73, 77, 89, 90], their identity appeared to differ in many ways. CoM exhibit a unique pyramidal-like structure composed of densely packed Iba1-positive cells encircling diseased or dying cells and independent of Aβ plaques. This organization suggests a significant perturbation to hippocampal integrity and circuitry, highlighting the potential role of CoM in the progression of neurodegeneration. Although sharing some similarities with PaM, such as the expression of CD68, CoM differ in their molecular machinery, particularly illustrated by their association with intraneuronal tau tangles or pSyn aggregates. While CoM express a distinct repertoire of markers associated with protein degradation, STING, TGF-β, NF-κB signaling, p53, and the SMAD ubiquitin-proteasome system (UPS), they notably lack markers indicative of T cell infiltrations. In our study, we did not find strong evidence for the involvement of CD8 + T cells, which were not significantly increased in AD compared to CTL and were not found near CoM or PaM. Furthermore, astrocytes surrounding CoM appeared dysmorphic without showing a specific disease-signature. The presence of CoM in the CA1 region in DLB cases, suggests a shared pathogenic mechanism underlying hippocampal neurodegeneration across diseases. Our results hint about a relationship between the disease state of pyramidal neurons, maturation of tau tangles and CoM [56]. However, elucidating the precise signaling cues driving CoM formation and their impact on disease severity requires further investigations.

Our study reveals complex interactions between PaM, PaM astrocytes, and CNS-associated macrophages in shaping the acute neurodegenerative microenvironment surrounding Aβ plaques in the hippocampi of AD patients. Notably, our findings are not restricted to the hippocampus, as we observed similar PaM signatures in other brain regions such as the entorhinal and temporal cortices. Our data confirmed the organisation of PaM and PaM astrocytes in RGN [9] in the hippocampus, with astrocytes enclosing PaM, themselves accumulating towards the core of the plaque. Our data show a positive correlation between both the number of PaM or PaM astrocytes with the size of the Aβ plaques, with a higher positive value for PaM. This suggests a sequential formation of the RGN with an early accumulation of PaM prior to the polarisation of surrounding astrocytes into a net. Furthermore, our investigation highlights the heterogeneity within the PaM population. Specifically, PaM (Iba1 + only) coexist with infiltrating CD163 + cells and double-positive for Iba1 and CD163 cells. CD163 + cells appear to be perivascular macrophages, whereas Iba1 + CD163 + cells may represent differentiated macrophages or microglia. Our findings are consistent with other recent studies showing higher CD163 expression in AD brain parenchyma [58, 62]. Our quantitative data suggest that the association of CD163 + and Iba1 + CD163 + with PaM and plaques is less frequent than the ones of PaM and PaM astrocytes and may be mediated by specific triggers or pathways. The definitive role of CD163 + macrophages at the plaque level isn’t defined by our study, but they may be prominent in orchestrating C1q production by PaM. De Schepper et al. have recently shown that secreted phosphoprotein 1 (SPP1/osteopontin) is mainly produced by perivascular macrophages and, when secreted, triggers C1q production by microglia and their phagocytic activity in an AD mouse model [17]. In the study, this mechanism was mainly described as a driver of abnormal synaptic pruning in AD. A similar increase in phagocytic activity may be relevant to Aβ clearance at the plaque level. Furthermore, our study uncovers the concomitant enrichment of C1q, C3, C4b and d, confirming what others have previously observed in the AD human brain [4, 21, 67, 71, 85]. Here, DSP and IHC image analysis, helped to associate C1q and C4 production with microglia [20] and C3 with astrocytes [45]. The C3 produced by astrocytes may play a dual role in limiting the spread of amyloid or in neurodegeneration. Indeed, Wyss-Coray et al. have shown that expression of soluble complement receptor-related protein y (sCrry), an inhibitor of C3 activation, in the APP/PS1 mouse model leads to a decrease in PaM activation, a 2-3-fold increase in amyloid load and a higher accumulation of degenerating neurons [100]. However, C3 is also associated with a neurotoxic signature of astrocytes [8, 45]. C1q, C3 and C4 are also associated with Wnt signaling [59], metabolic disorders, and insulin resistance pathways found in our ORA of PaM DEGs, or cholesterol metabolism found in our ORA of PaM astrocytes DEGs [79].

Moreover, PaM astrocytes express both PCSK9 and ITGA6, which, along C3, may influence microglial functions. PCSK9, known to regulate low-density lipoprotein (LDL) cholesterol levels, is expressed by hippocampal neurons in control and AD conditions, but also by PaM astrocytes in AD samples. Elevated PCSK9 RNA and protein levels have been reported in cortical tissue of AD patients compared to CTL [63], and its increase in cerebrospinal fluid (CSF) of AD patients correlates with ApoE, Aβ42 and pTau. PCSK9 may have multiple local effects on amyloid clearance, lipid metabolism and neuroinflammation. Briefly, PCSK9 influences beta-site amyloid precursor protein (APP)-cleaving enzyme 1 (BACE1) levels [34]. It has a direct effect on astrocyte cholesterol metabolism, increasing its synthesis and decreasing ApoE-HDL (high density lipoprotein) derived cholesterol uptake in vitro [61]. In the same study, authors found that it promotes an Aβ neurotoxic effect on neurons. PCSK9 may also play a role in the pro-inflammatory response of microglia, as shown in vitro [97]. Genetic deletion of PCSK9 in the 5xFAD mouse model attenuates Aβ burden, microglial activation, astrocytosis, and cognitive deficits, highlighting its potential as a therapeutic target in AD [95]. PCSK9 is also involved in modulating the pro-inflammatory state of macrophages and the inflammasome response [35, 69, 98]. The role of the astrocyte-secreted PCSK9 at the level of Aβ plaques may then be critical in altering local cholesterol metabolism, glial and immune cell states. In addition to PCSK9, our study identifies ITGA6, a cell surface receptor implicated in blood-brain barrier maintenance and inflammation [14], as being expressed by PaM astrocytes. ITGA6, also called CD49f, is expressed by a subpopulation of human iPSC-derived astrocytes in vitro [5, 94] and is shown here in RGN astrocytes, possibly related to their reactive profile. We also report ErbB2-ErbB4-positive PaM and observed erbB4-positive astrocytes in AD hippocampus. ErbB signaling is involved in astrocyte reactivity [13] and erbB4 has been found to be expressed in the vicinity of plaques in association with neuronal NRG1 [12]. ErbB signaling may also play a role in the activation state of both microglia and macrophages [46, 76].

The encapsulation of plaques by microglia, perivascular macrophages, and surrounding polarised astrocytes orchestrates a neurodegenerative micro-environment involved in disease progression[9]. We postulate that prolonged exposure of neurons trapped in RGN to molecules secreted by PaM and PaM astrocytes is likely to accelerate their degeneration. As evidenced by the high levels of P-Akt1 (S423) [15], the presence of swollen neurites and severe tau hyperphosphorylation observed in the hippocampal and cortical RGN [91]. Additionally, the detection of markers indicative of necroptosis further underscores the detrimental impact of this neurodegenerative niche. In this study, however, we do not observe any association between T cell infiltration and RGN or CoM [55]. The distinct molecular signature of rod-shaped microglia, which can also regroup in a CoM-like structure, confirms a heterogeneity of states in AD and emphasises the diversity of microglial responses.

Our study identifies distinct microglial aggregates, PaM and CoM, in the AD hippocampus, each with unique pathological and molecular signatures. Using deep spatial profiling and advanced microscopy, we reveal that CoM are associated with tau and α-synuclein pathology and protein degradation, while PaM are associated with amyloid deposition, complement pathways, and immune cell infiltration. We also identify new astrocyte signatures that shape the peri-plaque microenvironment. These findings elucidate complex glial-immune interactions driving neuroinflammation and hippocampal degeneration, providing a foundation for novel therapeutic strategies targeting glial and immune cells to mitigate AD progression.

Our DSP analysis was conducted on a limited number of samples and brain regions obtained from fixed post-mortem human brains. This limited sampling may not fully capture the heterogeneity of AD pathology, PaM and CoM, across different disease stages and brain regions. Moreover, the DSP approach, while powerful, is not without its limitations. It may not fully capture the specific molecular signatures of individual cell types. To partially address this issue, we performed a general validation of identified markers using IHC on a larger set of samples using different methodological approaches.

AA: amyloid angiopathy; ACADS: Acyl-CoA dehydrogenase; AD: Alzheimer’s disease; ADRD: Alzheimer’s disease related dementias; AIF1: allograft inflammatory factor 1; ALS: Amyotrophic lateral sclerosis; ALDOC: aldolase C fructose-bisphosphate; AOI: Areas of illumination; AP: Alk Phos; ApoE: Apolipoprotein E; APP: amyloid precursor protein; ARG1: arginase 1; Aβ: Amyloid-β; BACE-1: beta-site APP-cleaving enzyme 1; CA: Cornu ammonis; CAA: cerebral amyloid angiopathy; CC1: cell conditioning 1; CC2: cell conditioning 2;CD68: cluster of differentiation 68; CM: ChromoMap; CNS: Central Nervous System; CoM: Coffin-like microglia; CoM-Rod: CoM formed predominantly by rod-shaped microglia; CSF: cerebrospinal fluid; CSF-1R: colony-stimulating factor-1 receptor; CTL: control; CTX: cortex; C4B: complement component 4B; C1q: complement component 1q; C3: complement component 3; DAB: 3, 3′-diaminobenzidine; DEGs: Differentially expressed genes; DEPs: Differentially expressed proteins; DG: dentate gyrus; DLB: Dementia with Lewy bodies; DSP: Deep Spatial Profiling; ENT: entorhinal; EZR: ezrin; FC: Fold change; FDR: False discovery rate; FFPE: formalin fixed paraffin embedded; GFAP: glial fibrillary acidic protein; GWAS: Genome-wise association studies;; GO: gene ontology; GZMA: Granzyme A; HDL: high density lipoprotein; HIER: heat induced epitope retrieval; HRP: horseradish peroxidase; Iba1: ionized calcium-binding adaptor molecule 1; IHC: immunohistochemistry; ISH: In situ hybridization; ITGA6: integrin subunit alpha 6; KEGG: Kyoto Encyclopedia of Genes and Genomes; LAG3: lymphocyte activating 3; LDL: low-density lipoprotein; LOAD: Late Onset AD; LOQ: limit of quantitation; MS: Multiple sclerosis; NDD: Neurodegenerative disease; NFT: Neurofibrillary tangles; NGS: Next Generation Sequencing; ORA: over-representation analysis; PaM: Plaque-associated microglia; PBS: phosphate-buffered saline; PCSK9: proprotein convertase subtilisin/kexin type 9; PFA: paraformaldehyde; PL: pyramidal layer; PMD: post-mortem delay; pMLKL: phosphorylated mixed lineage kinase domain-like protein; PR: progesterone receptor; pSyn: Phosphorylated α-synuclein; pTau: Hyperphosphorylated tau; Q3: upper quartile; RGN: Reactive Glia Net; RT: room temperature; sCrry: soluble complement receptor-related protein y; SPP1: secreted phosphoprotein 1; STED: super resolution stimulated emission depletion microscopy; STING: stimulator of interferon genes; TDP-43: TAR DNA-binding protein; TGF: transforming growth factor; TR: Thiazine red; UMI: Unique Molcular Identifier; UPS: ubiquitin-proteasome system; UV: ultraviolet; WAM: white matter-associated microglia; WP: Wikipathways; 3D: three dimension .

Ethics approval

Use of human brain post-mortem samples for research was approved by the respective Ethic Panels of the Douglas-Bell Canada Brain Bank (Douglas Mental Health University Institute, Montreal, QC, Canada) and the GIE-Neuro-CEB biobank (Groupe Hospitalier Pitié-Salpêtrière, Paris, France) as well as of the University of Luxembourg (ERP 16-037 and 21-009).

Data availability

Raw DSP data is available on request.

Consent for publication

All authors have consented for the publication of manuscript.

Declaration of interests

The authors report no competing interests.

Funding

This work was supported by the Espoir-en-tête Rotary-International awards, the Auguste et Simone Prévot foundation, the Agaajani family donation for Alzheimer’s Disease research and the Fondation pour la Recherche contre Alzheimer (to DSB) and the Luxembourg National Research Fund (FNR: PEARL P16/BM/11192868 grant) (to MM). SF and MMM were supported by the PRIDE program of the Luxembourg National Research Found through the grants PRIDE17/12244779/PARK-QC and PRIDE21/16749720/NEXTIMMUNE2, respectively. FJ and SSc were supported by the AFR program of the Luxembourg National Research Found through the grants AFR15735457 and AFR17129900.

Acknowledgments

We thank all colleagues of the Luxembourg Center of Neuropathology, Dr Alexander Skupin, Prof. M.Heneka from the LCSB, the brain banks for their invaluable resource, their personnel (Douglas-Bell Canada Brain Bank (Douglas Mental Health University Institute, Montreal, QC, Canada) and GIE-Neuro-CEB biobank (Groupe Hospitalier Pitié-Salpêtrière, Paris, France), brain donors and their family and the team of Nanostring for their support of the project.

Author contributions

DSB conceived and supervised the study. SF, MMM, FJ, SSc, JJG and DSB performed the experiments. GH, SF and DSB produced and analyzed the statistical data. SF, MMM, GH and DSB prepared the figures. NM provided cohort of human brain samples. SF, MMM, GH, MM and DSB wrote the manuscript. All authors contributed to the final version of the manuscript.

Adler DH, Wisse LEM, Ittyerah R, Pluta JB, Ding SL, Xie L, Wang J, Kadivar S, Robinson JL, Schuck T, Trojanowski JQ, Grossman M, Detre JA, Elliott MA, Toledo JB, Liu W, Pickup S, Miller MI, Das SR, Wolk DA, Yushkevich PA (2018) Characterizing the human hippocampus in aging and Alzheimer’s disease using a computational atlas derived from ex vivo MRI and histology. Proceedings of the National Academy of Sciences of the United States of America 115. doi: 10.1073/pnas.1801093115
Alzheimer A (1911) Über eigenartige Krankheitsfälle des späteren Alters.
Apostolova LG, Mosconi L, Thompson PM, Green AE, Hwang KS, Ramirez A, Mistur R, Tsui WH, Leon MJ de (2010) Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiology of Aging 31. doi: 10.1016/j.neurobiolaging.2008.08.008
Bai B, Vanderwall D, Li Y, Wang X, Poudel S, Wang H, Dey KK, Chen P-C, Yang K, Peng J (2021) Proteomic landscape of Alzheimer’s Disease: novel insights into pathogenesis and biomarker discovery. Mol Neurodegeneration 16:55. doi: 10.1186/s13024-021-00474-z
Barbar L, Jain T, Zimmer M, Kruglikov I, Sadick JS, Wang M, Kalpana K, Rose IVL, Burstein SR, Rusielewicz T, Nijsure M, Guttenplan KA, Di Domenico A, Croft G, Zhang B, Nobuta H, Hébert JM, Liddelow SA, Fossati V (2020) CD49f Is a Novel Marker of Functional and Reactive Human iPSC-Derived Astrocytes. Neuron 107:436-453.e12. doi: 10.1016/j.neuron.2020.05.014
Bellenguez C (2022) New insights into the genetic etiology of Alzheimer’s disease and related dementias. Nature Genetics 54:33
Benitez DP, Jiang S, Wood J, Wang R, Hall CM, Peerboom C, Wong N, Stringer KM, Vitanova KS, Smith VC, Joshi D, Saito T, Saido TC, Hardy J, Hanrieder J, De Strooper B, Salih DA, Tripathi T, Edwards FA, Cummings DM (2021) Knock-in models related to Alzheimer’s disease: synaptic transmission, plaques and the role of microglia. Mol Neurodegeneration 16:47. doi: 10.1186/s13024-021-00457-0
Bouvier DS, Fixemer S, Heurtaux T, Jeannelle F, Frauenknecht KBM, Mittelbronn M (2022) The Multifaceted Neurotoxicity of Astrocytes in Ageing and Age-Related Neurodegenerative Diseases: A Translational Perspective. Front Physiol 13:814889. doi: 10.3389/fphys.2022.814889
Bouvier DS, Jones EV, Quesseveur G, Davoli MA, A. Ferreira T, Quirion R, Mechawar N, Murai KK (2016) High Resolution Dissection of Reactive Glial Nets in Alzheimer’s Disease. Sci Rep 6:24544. doi: 10.1038/srep24544
Braak H, Braak E (1991) Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol 82:239–259. doi: 10.1007/BF00308809
Burm SM, Peferoen LAN, Zuiderwijk-Sick EA, Haanstra KG, ’t Hart BA, van der Valk P, Amor S, Bauer J, Bajramovic JJ (2016) Expression of IL-1β in rhesus EAE and MS lesions is mainly induced in the CNS itself. J Neuroinflammation 13:138. doi: 10.1186/s12974-016-0605-8
Chaudhury AR, Gerecke KM, Wyss JM, Morgan DG, Gordon MN, Carroll SL (2003) Neuregulin-1 and ErbB4 Immunoreactivity Is Associated with Neuritic Plaques in Alzheimer Disease Brain and in a Transgenic Model of Alzheimer Disease. J Neuropathol Exp Neurol 62:42–54. doi: 10.1093/jnen/62.1.42
Chen J, He W, Hu X, Shen Y, Cao J, Wei Z, Luan Y, He L, Jiang F, Tao Y (2017) A role for ErbB signaling in the induction of reactive astrogliosis. Cell Discov 3:17044. doi: 10.1038/celldisc.2017.44
Chen MB, Yang AC, Yousef H, Lee D, Chen W, Schaum N, Lehallier B, Quake SR, Wyss-Coray T (2020) Brain Endothelial Cells Are Exquisite Sensors of Age-Related Circulatory Cues. Cell Reports 30:4418-4432.e4. doi: 10.1016/j.celrep.2020.03.012
Chu E, Mychasiuk R, Hibbs ML, Semple BD (2021) Dysregulated phosphoinositide 3-kinase signaling in microglia: shaping chronic neuroinflammation. J Neuroinflammation 18:276. doi: 10.1186/s12974-021-02325-6
Condello C, Yuan P, Schain A, Grutzendler J (2015) Microglia constitute a barrier that prevents neurotoxic protofibrillar Aβ42 hotspots around plaques. Nat Commun 6:6176. doi: 10.1038/ncomms7176
De Schepper S, Ge JZ, Crowley G, Ferreira LSS, Garceau D, Toomey CE, Sokolova D, Rueda-Carrasco J, Shin S-H, Kim J-S, Childs T, Lashley T, Burden JJ, Sasner M, Sala Frigerio C, Jung S, Hong S (2023) Perivascular cells induce microglial phagocytic states and synaptic engulfment via SPP1 in mouse models of Alzheimer’s disease. Nat Neurosci 26:406–415. doi: 10.1038/s41593-023-01257-z
Fendrick SE, Xue Q-S, Streit WJ (2007) Formation of multinucleated giant cells and microglial degeneration in rats expressing a mutant Cu/Zn superoxide dismutase gene. J Neuroinflammation 4:9. doi: 10.1186/1742-2094-4-9
Fixemer S, Ameli C, Hammer G, Salamanca L, Uriarte Huarte O, Schwartz C, Gérardy J-J, Mechawar N, Skupin A, Mittelbronn M, Bouvier DS (2022) Microglia phenotypes are associated with subregional patterns of concomitant tau, amyloid-β and α-synuclein pathologies in the hippocampus of patients with Alzheimer’s disease and dementia with Lewy bodies. acta neuropathol commun 10:36. doi: 10.1186/s40478-022-01342-7
Fonseca MI, Chu S-H, Hernandez MX, Fang MJ, Modarresi L, Selvan P, MacGregor GR, Tenner AJ (2017) Cell-specific deletion of C1qa identifies microglia as the dominant source of C1q in mouse brain. J Neuroinflammation 14:48. doi: 10.1186/s12974-017-0814-9
Fonseca MI, Kawas CH, Troncoso JC, Tenner AJ (2004) Neuronal localization of C1q in preclinical Alzheimer’s disease. Neurobiology of Disease 15:40–46. doi: 10.1016/j.nbd.2003.09.004
Frigerio CS, Wolfs L, Fattorelli N, Thrupp N, Voytyuk I, Schmidt I, Mancuso R, Chen WT, Woodbury ME, Srivastava G, Möller T, Hudry E, Das S, Saido T, Karran E, Hyman B, Perry VH, Fiers M, Strooper BD (2019) The Major Risk Factors for Alzheimer’s Disease: Age, Sex, and Genes Modulate the Microglia Response to Aβ Plaques. Cell Reports 27. doi: 10.1016/j.celrep.2019.03.099
Fu R, Shen Q, Xu P, Luo JJ, Tang Y (2014) Phagocytosis of Microglia in the Central Nervous System Diseases. Mol Neurobiol 49:1422–1434. doi: 10.1007/s12035-013-8620-6
Gagel O, Bodechtel G (1929) Die Topik und feinere Histologie der Ganglienzellgruppen in der Medulla oblongata und im Ponsgebiet mit einem kurzen Hinweis auf die Gliaverhältnisse und die Histopathologie
Gerrits E, Brouwer N, Kooistra SM, Woodbury ME, Vermeiren Y, Lambourne M, Mulder J, Kummer M, Möller T, Biber K, Dunnen WFA den, De Deyn PP, Eggen BJL, Boddeke EWGM (2021) Distinct amyloid-β and tau-associated microglia profiles in Alzheimer’s disease. Acta Neuropathol 141:681–696. doi: 10.1007/s00401-021-02263-w
Golde TE, Borchelt DR, Giasson BI, Lewis J (2013) Thinking laterally about neurodegenerative proteinopathies. Journal of Clinical Investigation. doi: 10.1172/JCI66029
Grubman A, Choo XY, Chew G, Ouyang JF, Sun G, Croft NP, Rossello FJ, Simmons R, Buckberry S, Landin DV, Pflueger J, Vandekolk TH, Abay Z, Zhou Y, Liu X, Chen J, Larcombe M, Haynes JM, McLean C, Williams S, Chai SY, Wilson T, Lister R, Pouton CW, Purcell AW, Rackham OJL, Petretto E, Polo JM (2021) Transcriptional signature in microglia associated with Aβ plaque phagocytosis. Nature Communications 12. doi: 10.1038/s41467-021-23111-1
Haney MS, Pálovics R, Munson CN, Long C, Johansson PK, Yip O, Dong W, Rawat E, West E, Schlachetzki JCM, Tsai A, Guldner IH, Lamichhane BS, Smith A, Schaum N, Calcuttawala K, Shin A, Wang Y-H, Wang C, Koutsodendris N, Serrano GE, Beach TG, Reiman EM, Glass CK, Abu-Remaileh M, Enejder A, Huang Y, Wyss-Coray T (2024) APOE4/4 is linked to damaging lipid droplets in Alzheimer’s disease microglia. Nature 628:154–161. doi: 10.1038/s41586-024-07185-7
Huang Y, Happonen KE, Burrola PG, O’Connor C, Hah N, Huang L, Nimmerjahn A, Lemke G (2021) Microglia use TAM receptors to detect and engulf amyloid β plaques. Nature Immunology 22. doi: 10.1038/s41590-021-00913-5
Huang Y, Wang M, Ni H, Zhang J, Li A, Hu B, Junqueira Alves C, Wahane S, Rios De Anda M, Ho L, Li Y, Kang S, Neff R, Kostic A, Buxbaum JD, Crary JF, Brennand KJ, Zhang B, Zou H, Friedel RH (2024) Regulation of cell distancing in peri-plaque glial nets by Plexin-B1 affects glial activation and amyloid compaction in Alzheimer’s disease. Nat Neurosci 27:1489–1504. doi: 10.1038/s41593-024-01664-w
Ising C, Venegas C, Zhang S, Scheiblich H, Schmidt SV, Vieira-Saecker A, Schwartz S, Albasset S, McManus RM, Tejera D, Griep A, Santarelli F, Brosseron F, Opitz S, Stunden J, Merten M, Kayed R, Golenbock DT, Blum D, Latz E, Buée L, Heneka MT (2019) NLRP3 inflammasome activation drives tau pathology. Nature 575:669–673. doi: 10.1038/s41586-019-1769-z
Jansen IE, Savage JE, Watanabe K, Bryois J, Williams DM, Steinberg S, Sealock J, Karlsson IK, Hägg S, Athanasiu L, Voyle N, Proitsi P, Witoelar A, Stringer S, Aarsland D, Almdahl IS, Andersen F, Bergh S, Bettella F, Bjornsson S, Brækhus A, Bråthen G, Leeuw C de, Desikan RS, Djurovic S, Dumitrescu L, Fladby T, Hohman TJ, Jonsson PV, Kiddle SJ, Rongve A, Saltvedt I, Sando SB, Selbæk G, Shoai M, Skene NG, Snaedal J, Stordal E, Ulstein ID, Wang Y, White LR, Hardy J, Hjerling-Leffler J, Sullivan PF, Flier WM van der, Dobson R, Davis LK, Stefansson H, Stefansson K, Pedersen NL, Ripke S, Andreassen OA, Posthuma D (2019) Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer’s disease risk. Nature Genetics 51:404–413. doi: 10.1038/s41588-018-0311-9
Jellinger KA, Attems J (2015) Challenges of multimorbidity of the aging brain: a critical update. J Neural Transm 122:505–521. doi: 10.1007/s00702-014-1288-x
Jonas MC, Costantini C, Puglielli L (2008) PCSK9 is required for the disposal of non‐acetylated intermediates of the nascent membrane protein BACE1. EMBO Reports 9:916–922. doi: 10.1038/embor.2008.132
Katsuki S, K. Jha P, Lupieri A, Nakano T, Passos LSA, Rogers MA, Becker-Greene D, Le T-D, Decano JL, Ho Lee L, Guimaraes GC, Abdelhamid I, Halu A, Muscoloni A, V. Cannistraci C, Higashi H, Zhang H, Vromman A, Libby P, Keith Ozaki C, Sharma A, Singh SA, Aikawa E, Aikawa M (2022) Proprotein Convertase Subtilisin/Kexin 9 (PCSK9) Promotes Macrophage Activation via LDL Receptor-Independent Mechanisms. Circulation Research 131:873–889. doi: 10.1161/CIRCRESAHA.121.320056
Kerchner GA, Deutsch GK, Zeineh M, Dougherty RF, Saranathan M, Rutt BK (2012) Hippocampal CA1 apical neuropil atrophy and memory performance in Alzheimer’s disease. NeuroImage 63. doi: 10.1016/j.neuroimage.2012.06.048
Keren-Shaul H, Spinrad A, Weiner A, Matcovitch-Natan O, Dvir-Szternfeld R, Ulland TK, David E, Baruch K, Lara-Astaiso D, Toth B, Itzkovitz S, Colonna M, Schwartz M, Amit I (2017) A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease. Cell 169:1276-1290.e17. doi: 10.1016/j.cell.2017.05.018
Klarfeld B (1922) Einige allgemeine Betrachtungen zur Histopathologie des Zentralnervensystems (auf Grund yon Untersuchungen iiber die Encephalitis epidemica). 82
Koper MJ, Schoor EV, Ospitalieri S, Vandenberghe R, Vandenbulcke M, Arnim CAF von, Tousseyn T, Balusu S, Strooper BD, Thal DR (2020) Necrosome complex detected in granulovacuolar degeneration is associated with neuronal loss in Alzheimer’s disease. Acta Neuropathologica 139:463–484. doi: 10.1007/s00401-019-02103-y
Krasemann S, Madore C, Cialic R, Baufeld C, Calcagno N, Fatimy RE, Beckers L, O’Loughlin E, Xu Y, Fanek Z, Greco DJ, Smith ST, Tweet G, Humulock Z, Zrzavy T, Conde-Sanroman P, Gacias M, Weng Z, Chen H, Tjon E, Mazaheri F, Hartmann K, Madi A, Ulrich JD, Glatzel M, Worthmann A, Heeren J, Budnik B, Lemere C, Ikezu T, Heppner FL, Litvak V, Holtzman DM, Lassmann H, Weiner HL, Ochando J, Haass C, Butovsky O (2017) The TREM2-APOE Pathway Drives the Transcriptional Phenotype of Dysfunctional Microglia in Neurodegenerative Diseases. Immunity 47. doi: 10.1016/j.immuni.2017.08.008
Kunkle BW, Grenier-Boley B, Sims R, Bis JC, Damotte V, Naj AC, Boland A, Vronskaya M, van der Lee SJ, Amlie-Wolf A, Bellenguez C, Frizatti A, Chouraki V, Martin ER, Sleegers K, Badarinarayan N, Jakobsdottir J, Hamilton-Nelson KL, Moreno-Grau S, Olaso R, Raybould R, Chen Y, Kuzma AB, Hiltunen M, Morgan T, Ahmad S, Vardarajan BN, Epelbaum J, Hoffmann P, Boada M, Beecham GW, Garnier J-G, Harold D, Fitzpatrick AL, Valladares O, Moutet M-L, Gerrish A, Smith AV, Qu L, Bacq D, Denning N, Jian X, Zhao Y, Del Zompo M, Fox NC, Choi S-H, Mateo I, Hughes JT, Adams HH, Malamon J, Sanchez-Garcia F, Patel Y, Brody JA, Dombroski BA, Naranjo MCD, Daniilidou M, Eiriksdottir G, Mukherjee S, Wallon D, Uphill J, Aspelund T, Cantwell LB, Garzia F, Galimberti D, Hofer E, Butkiewicz M, Fin B, Scarpini E, Sarnowski C, Bush WS, Meslage S, Kornhuber J, White CC, Song Y, Barber RC, Engelborghs S, Sordon S, Voijnovic D, Adams PM, Vandenberghe R, Mayhaus M, Cupples LA, Albert MS, De Deyn PP, Gu W, Himali JJ, Beekly D, Squassina A, Hartmann AM, Orellana A, Blacker D, Rodriguez-Rodriguez E, Lovestone S, Garcia ME, Doody RS, Munoz-Fernadez C, Sussams R, Lin H, Fairchild TJ, Benito YA, Holmes C, Karamujić-Čomić H, Frosch MP, Thonberg H, Maier W, Roshchupkin G, Ghetti B, Giedraitis V, Kawalia A, Li S, Huebinger RM, Kilander L, Moebus S, Hernández I, Kamboh MI, Brundin R, Turton J, Yang Q, Katz MJ, Concari L, Lord J, Beiser AS, Keene CD, Helisalmi S, Kloszewska I, Kukull WA, Koivisto AM, Lynch A, Tarraga L, Larson EB, Haapasalo A, Lawlor B, Mosley TH, Lipton RB, Solfrizzi V, Gill M, Longstreth WT, Montine TJ, Frisardi V, Diez-Fairen M, Rivadeneira F, Petersen RC, Deramecourt V, Alvarez I, Salani F, Ciaramella A, Boerwinkle E, Reiman EM, Fievet N, Rotter JI, Reisch JS, Hanon O, Cupidi C, Andre Uitterlinden AG, Royall DR, Dufouil C, Maletta RG, de Rojas I, Sano M, Brice A, Cecchetti R, George-Hyslop PS, Ritchie K, Tsolaki M, Tsuang DW, Dubois B, Craig D, Wu C-K, Soininen H, Avramidou D, Albin RL, Fratiglioni L, Germanou A, Apostolova LG, Keller L, Koutroumani M, Arnold SE, Panza F, Gkatzima O, Asthana S, Hannequin D, Whitehead P, Atwood CS, Caffarra P, Hampel H, Quintela I, Carracedo Á, Lannfelt L, Rubinsztein DC, Barnes LL, Pasquier F, Frölich L, Barral S, McGuinness B, Beach TG, Johnston JA, Becker JT, Passmore P, Bigio EH, Schott JM, Bird TD, Warren JD, Boeve BF, Lupton MK, Bowen JD, Proitsi P, Boxer A, Powell JF, Burke JR, Kauwe JSK, Burns JM, Mancuso M, Buxbaum JD, Bonuccelli U, Cairns NJ, McQuillin A, Cao C, Livingston G, Carlson CS, Bass NJ, Carlsson CM, Hardy J, Carney RM, Bras J, Carrasquillo MM, Guerreiro R, Allen M, Chui HC, Fisher E, Masullo C, Crocco EA, DeCarli C, Bisceglio G, Dick M, Ma L, Duara R, Graff-Radford NR, Evans DA, Hodges A, Faber KM, Scherer M, Fallon KB, Riemenschneider M, Fardo DW, Heun R, Farlow MR, Kölsch H, Ferris S, Leber M, Foroud TM, Heuser I, Galasko DR, Giegling I, Gearing M, Hüll M, Geschwind DH, Gilbert JR, Morris J, Green RC, Mayo K, Growdon JH, Feulner T, Hamilton RL, Harrell LE, Drichel D, Honig LS, Cushion TD, Huentelman MJ, Hollingworth P, Hulette CM, Hyman BT, Marshall R, Jarvik GP, Meggy A, Abner E, Menzies GE, Jin L-W, Leonenko G, Real LM, Jun GR, Baldwin CT, Grozeva D, Karydas A, Russo G, Kaye JA, Kim R, Jessen F, Kowall NW, Vellas B, Kramer JH, Vardy E, LaFerla FM, Jöckel K-H, Lah JJ, Dichgans M, Leverenz JB, Mann D, Levey AI, Pickering-Brown S, Lieberman AP, Klopp N, Lunetta KL, Wichmann H-E, Lyketsos CG, Morgan K, Marson DC, Brown K, Martiniuk F, Medway C, Mash DC, Nöthen MM, Masliah E, Hooper NM, McCormick WC, Daniele A, McCurry SM, Bayer A, McDavid AN, Gallacher J, McKee AC, van den Bussche H, Mesulam M, Brayne C, Miller BL, Riedel-Heller S, Miller CA, Miller JW, Al-Chalabi A, Morris JC, Shaw CE, Myers AJ, Wiltfang J, O’Bryant S, Olichney JM, Alvarez V, Parisi JE, Singleton AB, Paulson HL, Collinge J, Perry WR, Mead S, Peskind E, Cribbs DH, Rossor M, Pierce A, Ryan NS, Poon WW, Nacmias B, Potter H, Sorbi S, Quinn JF, Sacchinelli E, Raj A, Spalletta G, Raskind M, Caltagirone C, Bossù P, Orfei MD, Reisberg B, Clarke R, Reitz C, Smith AD, Ringman JM, Warden D, Roberson ED, Wilcock G, Rogaeva E, Bruni AC, Rosen HJ, Gallo M, Rosenberg RN, Ben-Shlomo Y, Sager MA, Mecocci P, Saykin AJ, Pastor P, Cuccaro ML, Vance JM, Schneider JA, Schneider LS, Slifer S, Seeley WW, Smith AG, Sonnen JA, Spina S, Stern RA, Swerdlow RH, Tang M, Tanzi RE, Trojanowski JQ, Troncoso JC, Van Deerlin VM, Van Eldik LJ, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Wilhelmsen KC, Williamson J, Wingo TS, Woltjer RL, Wright CB, Yu C-E, Yu L, Saba Y, Pilotto A, Bullido MJ, Peters O, Crane PK, Bennett D, Bosco P, Coto E, Boccardi V, De Jager PL, Lleo A, Warner N, Lopez OL, Ingelsson M, Deloukas P, Cruchaga C, Graff C, Gwilliam R, Fornage M, Goate AM, Sanchez-Juan P, Kehoe PG, Amin N, Ertekin-Taner N, Berr C, Debette S, Love S, Launer LJ, Younkin SG, Dartigues J-F, Corcoran C, Ikram MA, Dickson DW, Nicolas G, Campion D, Tschanz J, Schmidt H, Hakonarson H, Clarimon J, Munger R, Schmidt R, Farrer LA, Van Broeckhoven C, C O’Donovan M, DeStefano AL, Jones L, Haines JL, Deleuze J-F, Owen MJ, Gudnason V, Mayeux R, Escott-Price V, Psaty BM, Ramirez A, Wang L-S, Ruiz A, van Duijn CM, Holmans PA, Seshadri S, Williams J, Amouyel P, Schellenberg GD, Lambert J-C, Pericak-Vance MA, Alzheimer Disease Genetics Consortium (ADGC), European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE),, Genetic and Environmental Risk in AD/Defining Genetic, Polygenic and Environmental Risk for Alzheimer’s Disease Consortium (GERAD/PERADES), (2019) Genetic meta-analysis of diagnosed Alzheimer’s disease identifies new risk loci and implicates Aβ, tau, immunity and lipid processing. Nat Genet 51:414–430. doi: 10.1038/s41588-019-0358-2
La Joie R, Perrotin A, de La Sayette V, Egret S, Doeuvre L, Belliard S, Eustache F, Desgranges B, Chételat G (2013) Hippocampal subfield volumetry in mild cognitive impairment, Alzheimer’s disease and semantic dementia. NeuroImage: Clinical 3:155–162. doi: 10.1016/j.nicl.2013.08.007
Ledonne A, Mercuri NB (2019) On the Modulatory Roles of Neuregulins/ErbB Signaling on Synaptic Plasticity. IJMS 21:275. doi: 10.3390/ijms21010275
Leng F, Edison P (2021) Neuroinflammation and microglial activation in Alzheimer disease: where do we go from here? Nat Rev Neurol 17:157–172. doi: 10.1038/s41582-020-00435-y
Liddelow SA, Guttenplan KA, Clarke LE, Bennett FC, Bohlen CJ, Schirmer L, Bennett ML, Münch AE, Chung W-S, Peterson TC, Wilton DK, Frouin A, Napier BA, Panicker N, Kumar M, Buckwalter MS, Rowitch DH, Dawson VL, Dawson TM, Stevens B, Barres BA (2017) Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541:481–487. doi: 10.1038/nature21029
Ma Y, Fan P, Zhao R, Zhang Y, Wang X, Cui W (2022) Neuregulin-1 regulates the conversion of M1/M2 microglia phenotype via ErbB4-dependent inhibition of the NF-κB pathway. Mol Biol Rep 49:3975–3986. doi: 10.1007/s11033-022-07249-9
Mak E, Su L, Williams GB, Watson R, Firbank M, Blamire A, O’Brien J (2016) Differential atrophy of hippocampal subfields: A comparative study of dementia with lewy bodies and Alzheimer disease. American Journal of Geriatric Psychiatry 24:136–143. doi: 10.1016/j.jagp.2015.06.006
Marx V (2021) Method of the Year: spatially resolved transcriptomics. Nat Methods 18:9–14. doi: 10.1038/s41592-020-01033-y
Masuda T, Sankowski R, Staszewski O, Prinz M (2020) Microglia Heterogeneity in the Single-Cell Era. Cell Reports 30:1271–1281. doi: 10.1016/j.celrep.2020.01.010
Mathys H, Davila-Velderrain J, Peng Z, Gao F, Mohammadi S, Young JZ, Menon M, He L, Abdurrob F, Jiang X, Martorell AJ, Ransohoff RM, Hafler BP, Bennett DA, Kellis M, Tsai L-H (2019) Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570:332–337. doi: 10.1038/s41586-019-1195-2
McAleese KE, Colloby SJ, Thomas AJ, Al‐Sarraj S, Ansorge O, Neal J, Roncaroli F, Love S, Francis PT, Attems J (2021) Concomitant neurodegenerative pathologies contribute to the transition from mild cognitive impairment to dementia. Alzheimer’s & Dementia 17:1121–1133. doi: 10.1002/alz.12291
McAleese KE, Walker L, Erskine D, Thomas AJ, McKeith IG, Attems J (2017) TDP-43 pathology in Alzheimer’s disease, dementia with Lewy bodies and ageing: TDP-43 pathology. Brain Pathology 27:472–479. doi: 10.1111/bpa.12424
McKeith IG (2006) Consensus guidelines for the clinical and pathologic diagnosis of dementia with Lewy bodies (DLB): Report of the Consortium on DLB International Workshop. JAD 9:417–423. doi: 10.3233/JAD-2006-9S347
Meneses A, Koga S, O’Leary J, Dickson DW, Bu G, Zhao N (2021) TDP-43 Pathology in Alzheimer’s Disease. Mol Neurodegeneration 16:84. doi: 10.1186/s13024-021-00503-x
Merlini M, Kirabali T, Kulic L, Nitsch RM, Ferretti MT (2018) Extravascular CD3+ T Cells in Brains of Alzheimer Disease Patients Correlate with Tau but Not with Amyloid Pathology: An Immunohistochemical Study. Neurodegener Dis 18:49–56. doi: 10.1159/000486200
Moloney CM, Lowe VJ, Murray ME (2021) Visualization of neurofibrillary tangle maturity in Alzheimer’s disease: A clinicopathologic perspective for biomarker research. Alzheimer’s & Dementia 17:1554–1574. doi: 10.1002/alz.12321
Montine TJ, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Dickson DW, Duyckaerts C, Frosch MP, Masliah E, Mirra SS, Nelson PT, Schneider JA, Thal DR, Trojanowski JQ, Vinters HV, Hyman BT (2012) National institute on aging-Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease: A practical approach. Acta Neuropathologica 123. doi: 10.1007/s00401-011-0910-3
Muñoz-Castro C, Mejias-Ortega M, Sanchez-Mejias E, Navarro V, Trujillo-Estrada L, Jimenez S, Garcia-Leon JA, Fernandez-Valenzuela JJ, Sanchez-Mico MV, Romero-Molina C, Moreno-Gonzalez I, Baglietto-Vargas D, Vizuete M, Gutierrez A, Vitorica J (2023) Monocyte-derived cells invade brain parenchyma and amyloid plaques in human Alzheimer’s disease hippocampus. acta neuropathol commun 11:31. doi: 10.1186/s40478-023-01530-z
Naito AT, Sumida T, Nomura S, Liu M-L, Higo T, Nakagawa A, Okada K, Sakai T, Hashimoto A, Hara Y, Shimizu I, Zhu W, Toko H, Katada A, Akazawa H, Oka T, Lee J-K, Minamino T, Nagai T, Walsh K, Kikuchi A, Matsumoto M, Botto M, Shiojima I, Komuro I (2012) Complement C1q Activates Canonical Wnt Signaling and Promotes Aging-Related Phenotypes. Cell 149:1298–1313. doi: 10.1016/j.cell.2012.03.047
Novikova G, Kapoor M, Tcw J, Abud EM, Efthymiou AG, Chen SX, Cheng H, Fullard JF, Bendl J, Liu Y, Roussos P, Björkegren JL, Liu Y, Poon WW, Hao K, Marcora E, Goate AM (2021) Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat Commun 12:1610. doi: 10.1038/s41467-021-21823-y
Papotti B, Adorni MP, Marchi C, Zimetti F, Ronda N, Panighel G, Lupo MG, Vilella A, Giuliani D, Ferri N, Bernini F (2022) PCSK9 Affects Astrocyte Cholesterol Metabolism and Reduces Neuron Cholesterol Supplying In Vitro: Potential Implications in Alzheimer’s Disease. IJMS 23:12192. doi: 10.3390/ijms232012192
Pey P, Pearce RK, Kalaitzakis ME, Griffin WST, Gentleman SM (2014) Phenotypic profile of alternative activation marker CD163 is different in Alzheimer’s and Parkinson’s disease. acta neuropathol commun 2:21. doi: 10.1186/2051-5960-2-21
Picard C, Poirier A, Bélanger S, Labonté A, Auld D, Poirier J, on behalf of the PREVENT-AD Research Group (2019) Proprotein convertase subtilisin/kexin type 9 (PCSK9) in Alzheimer’s disease: A genetic and proteomic multi-cohort study. PLoS ONE 14:e0220254. doi: 10.1371/journal.pone.0220254
Pini L, Pievani M, Bocchetta M, Altomare D, Bosco P, Cavedo E, Galluzzi S, Marizzoni M, Frisoni GB (2016) Brain atrophy in Alzheimer’s Disease and aging. Ageing Research Reviews 30:25–48. doi: 10.1016/j.arr.2016.01.002
Quesseveur G, Fouquier d’Hérouël A, Murai KK, Bouvier DS (2019) A Specialized Method to Resolve Fine 3D Features of Astrocytes in Nonhuman Primate (Marmoset, Callithrix jacchus) and Human Fixed Brain Samples. In: Di Benedetto B (ed) Astrocytes. Springer New York, New York, NY, pp 85–95
Rahimi J, Kovacs GG (2014) Prevalence of mixed pathologies in the aging brain. Alz Res Therapy 6:82. doi: 10.1186/s13195-014-0082-1
Rahman MM, Lendel C (2021) Extracellular protein components of amyloid plaques and their roles in Alzheimer’s disease pathology. Mol Neurodegeneration 16:59. doi: 10.1186/s13024-021-00465-0
Rangaraju S, Dammer EB, Raza SA, Gao T, Xiao H, Betarbet R, Duong DM, Webster JA, Hales CM, Lah JJ, Levey AI, Seyfried NT (2018) Quantitative proteomics of acutely-isolated mouse microglia identifies novel immune Alzheimer’s disease-related proteins. Mol Neurodegeneration 13:34. doi: 10.1186/s13024-018-0266-4
Ricci C, Ruscica M, Camera M, Rossetti L, Macchi C, Colciago A, Zanotti I, Lupo MG, Adorni MP, Cicero AFG, Fogacci F, Corsini A, Ferri N (2018) PCSK9 induces a pro-inflammatory response in macrophages. Sci Rep 8:2267. doi: 10.1038/s41598-018-20425-x
Robinson JL, Lee EB, Xie SX, Rennert L, Suh E, Bredenberg C, Caswell C, Van Deerlin VM, Yan N, Yousef A, Hurtig HI, Siderowf A, Grossman M, McMillan CT, Miller B, Duda JE, Irwin DJ, Wolk D, Elman L, McCluskey L, Chen-Plotkin A, Weintraub D, Arnold SE, Brettschneider J, Lee VM-Y, Trojanowski JQ (2018) Neurodegenerative disease concomitant proteinopathies are prevalent, age-related and APOE4-associated. Brain 141:2181–2193. doi: 10.1093/brain/awy146
Rogers J, Cooper NR, Webster S, Schultz J, McGeer PL, Styren SD, Civin WH, Brachova L, Bradt B, Ward P (1992) Complement activation by beta-amyloid in Alzheimer disease. Proc Natl Acad Sci USA 89:10016–10020. doi: 10.1073/pnas.89.21.10016
Sabattoli F, Boccardi M, Galluzzi S, Treves A, Thompson PM, Frisoni GB (2008) Hippocampal shape differences in dementia with Lewy bodies. NeuroImage 41:699–705. doi: 10.1016/j.neuroimage.2008.02.060
Safaiyan S, Besson-Girard S, Kaya T, Cantuti-Castelvetri L, Liu L, Ji H, Schifferer M, Gouna G, Usifo F, Kannaiyan N, Fitzner D, Xiang X, Rossner MJ, Brendel M, Gokce O, Simons M (2021) White matter aging drives microglial diversity. Neuron 109:1100-1117.e10. doi: 10.1016/j.neuron.2021.01.027
Salamanca L, Mechawar N, Murai KK, Balling R, Bouvier DS, Skupin A (2019) MIC-MAC: An automated pipeline for high-throughput characterization and classification of three-dimensional microglia morphologies in mouse and human postmortem brain samples. GLIA 67. doi: 10.1002/glia.23623
Salter MW, Stevens B (2017) Microglia emerge as central players in brain disease. Nat Med 23:1018–1027. doi: 10.1038/nm.4397
Schumacher L, Slimani R, Zizmare L, Ehlers J, Kleine Borgmann F, Fitzgerald JC, Fallier-Becker P, Beckmann A, Grißmer A, Meier C, El-Ayoubi A, Devraj K, Mittelbronn M, Trautwein C, Naumann U (2023) TGF-Beta Modulates the Integrity of the Blood Brain Barrier In Vitro, and Is Associated with Metabolic Alterations in Pericytes. Biomedicines 11:214. doi: 10.3390/biomedicines11010214
Schwabenland M, Salié H, Tanevski J, Killmer S, Lago MS, Schlaak AE, Mayer L, Matschke J, Püschel K, Fitzek A, Ondruschka B, Mei HE, Boettler T, Neumann-Haefelin C, Hofmann M, Breithaupt A, Genc N, Stadelmann C, Saez-Rodriguez J, Bronsert P, Knobeloch K-P, Blank T, Thimme R, Glatzel M, Prinz M, Bengsch B (2021) Deep spatial profiling of human COVID-19 brains reveals neuroinflammation with distinct microanatomical microglia-T-cell interactions. Immunity 54:1594-1610.e11. doi: 10.1016/j.immuni.2021.06.002
Sen T, Saha P, Jiang T, Sen N (2020) Sulfhydration of AKT triggers Tau-phosphorylation by activating glycogen synthase kinase 3β in Alzheimer’s disease. Proc Natl Acad Sci USA 117:4418–4427. doi: 10.1073/pnas.1916895117
Shim K, Begum R, Yang C, Wang H (2020) Complement activation in obesity, insulin resistance, and type 2 diabetes mellitus. WJD 11:1–12. doi: 10.4239/wjd.v11.i1.1
Shippy DC, Ulland TK (2023) Lipid metabolism transcriptomics of murine microglia in Alzheimer’s disease and neuroinflammation. Sci Rep 13:14800. doi: 10.1038/s41598-023-41897-6
Sierra A, Encinas JM, Deudero JJP, Chancey JH, Enikolopov G, Overstreet-Wadiche LS, Tsirka SE, Maletic-Savatic M (2010) Microglia Shape Adult Hippocampal Neurogenesis through Apoptosis-Coupled Phagocytosis. Cell Stem Cell 7:483–495. doi: 10.1016/j.stem.2010.08.014
Sims R, van der Lee SJ, Naj AC, Bellenguez C, Badarinarayan N, Jakobsdottir J, Kunkle BW, Boland A, Raybould R, Bis JC, Martin ER, Grenier-Boley B, Heilmann-Heimbach S, Chouraki V, Kuzma AB, Sleegers K, Vronskaya M, Ruiz A, Graham RR, Olaso R, Hoffmann P, Grove ML, Vardarajan BN, Hiltunen M, Nöthen MM, White CC, Hamilton-Nelson KL, Epelbaum J, Maier W, Choi S-H, Beecham GW, Dulary C, Herms S, Smith AV, Funk CC, Derbois C, Forstner AJ, Ahmad S, Li H, Bacq D, Harold D, Satizabal CL, Valladares O, Squassina A, Thomas R, Brody JA, Qu L, Sánchez-Juan P, Morgan T, Wolters FJ, Zhao Y, Garcia FS, Denning N, Fornage M, Malamon J, Naranjo MCD, Majounie E, Mosley TH, Dombroski B, Wallon D, Lupton MK, Dupuis J, Whitehead P, Fratiglioni L, Medway C, Jian X, Mukherjee S, Keller L, Brown K, Lin H, Cantwell LB, Panza F, McGuinness B, Moreno-Grau S, Burgess JD, Solfrizzi V, Proitsi P, Adams HH, Allen M, Seripa D, Pastor P, Cupples LA, Price ND, Hannequin D, Frank-García A, Levy D, Chakrabarty P, Caffarra P, Giegling I, Beiser AS, Giedraitis V, Hampel H, Garcia ME, Wang X, Lannfelt L, Mecocci P, Eiriksdottir G, Crane PK, Pasquier F, Boccardi V, Henández I, Barber RC, Scherer M, Tarraga L, Adams PM, Leber M, Chen Y, Albert MS, Riedel-Heller S, Emilsson V, Beekly D, Braae A, Schmidt R, Blacker D, Masullo C, Schmidt H, Doody RS, Spalletta G, Longstreth WT, Fairchild TJ, Bossù P, Lopez OL, Frosch MP, Sacchinelli E, Ghetti B, Yang Q, Huebinger RM, Jessen F, Li S, Kamboh MI, Morris J, Sotolongo-Grau O, Katz MJ, Corcoran C, Dunstan M, Braddel A, Thomas C, Meggy A, Marshall R, Gerrish A, Chapman J, Aguilar M, Taylor S, Hill M, Fairén MD, Hodges A, Vellas B, Soininen H, Kloszewska I, Daniilidou M, Uphill J, Patel Y, Hughes JT, Lord J, Turton J, Hartmann AM, Cecchetti R, Fenoglio C, Serpente M, Arcaro M, Caltagirone C, Orfei MD, Ciaramella A, Pichler S, Mayhaus M, Gu W, Lleó A, Fortea J, Blesa R, Barber IS, Brookes K, Cupidi C, Maletta RG, Carrell D, Sorbi S, Moebus S, Urbano M, Pilotto A, Kornhuber J, Bosco P, Todd S, Craig D, Johnston J, Gill M, Lawlor B, Lynch A, Fox NC, Hardy J, ARUK Consortium, Albin RL, Apostolova LG, Arnold SE, Asthana S, Atwood CS, Baldwin CT, Barnes LL, Barral S, Beach TG, Becker JT, Bigio EH, Bird TD, Boeve BF, Bowen JD, Boxer A, Burke JR, Burns JM, Buxbaum JD, Cairns NJ, Cao C, Carlson CS, Carlsson CM, Carney RM, Carrasquillo MM, Carroll SL, Diaz CC, Chui HC, Clark DG, Cribbs DH, Crocco EA, DeCarli C, Dick M, Duara R, Evans DA, Faber KM, Fallon KB, Fardo DW, Farlow MR, Ferris S, Foroud TM, Galasko DR, Gearing M, Geschwind DH, Gilbert JR, Graff-Radford NR, Green RC, Growdon JH, Hamilton RL, Harrell LE, Honig LS, Huentelman MJ, Hulette CM, Hyman BT, Jarvik GP, Abner E, Jin L-W, Jun G, Karydas A, Kaye JA, Kim R, Kowall NW, Kramer JH, LaFerla FM, Lah JJ, Leverenz JB, Levey AI, Li G, Lieberman AP, Lunetta KL, Lyketsos CG, Marson DC, Martiniuk F, Mash DC, Masliah E, McCormick WC, McCurry SM, McDavid AN, McKee AC, Mesulam M, Miller BL, Miller CA, Miller JW, Morris JC, Murrell JR, Myers AJ, O’Bryant S, Olichney JM, Pankratz VS, Parisi JE, Paulson HL, Perry W, Peskind E, Pierce A, Poon WW, Potter H, Quinn JF, Raj A, Raskind M, Reisberg B, Reitz C, Ringman JM, Roberson ED, Rogaeva E, Rosen HJ, Rosenberg RN, Sager MA, Saykin AJ, Schneider JA, Schneider LS, Seeley WW, Smith AG, Sonnen JA, Spina S, Stern RA, Swerdlow RH, Tanzi RE, Thornton-Wells TA, Trojanowski JQ, Troncoso JC, Van Deerlin VM, Van Eldik LJ, Vinters HV, Vonsattel JP, Weintraub S, Welsh-Bohmer KA, Wilhelmsen KC, Williamson J, Wingo TS, Woltjer RL, Wright CB, Yu C-E, Yu L, Garzia F, Golamaully F, Septier G, Engelborghs S, Vandenberghe R, De Deyn PP, Fernadez CM, Benito YA, Thonberg H, Forsell C, Lilius L, Kinhult-Stählbom A, Kilander L, Brundin R, Concari L, Helisalmi S, Koivisto AM, Haapasalo A, Dermecourt V, Fievet N, Hanon O, Dufouil C, Brice A, Ritchie K, Dubois B, Himali JJ, Keene CD, Tschanz J, Fitzpatrick AL, Kukull WA, Norton M, Aspelund T, Larson EB, Munger R, Rotter JI, Lipton RB, Bullido MJ, Hofman A, Montine TJ, Coto E, Boerwinkle E, Petersen RC, Alvarez V, Rivadeneira F, Reiman EM, Gallo M, O’Donnell CJ, Reisch JS, Bruni AC, Royall DR, Dichgans M, Sano M, Galimberti D, St George-Hyslop P, Scarpini E, Tsuang DW, Mancuso M, Bonuccelli U, Winslow AR, Daniele A, Wu C-K, GERAD/PERADES, CHARGE, ADGC, EADI, Peters O, Nacmias B, Riemenschneider M, Heun R, Brayne C, Rubinsztein DC, Bras J, Guerreiro R, Al-Chalabi A, Shaw CE, Collinge J, Mann D, Tsolaki M, Clarimón J, Sussams R, Lovestone S, O’Donovan MC, Owen MJ, Behrens TW, Mead S, Goate AM, Uitterlinden AG, Holmes C, Cruchaga C, Ingelsson M, Bennett DA, Powell J, Golde TE, Graff C, De Jager PL, Morgan K, Ertekin-Taner N, Combarros O, Psaty BM, Passmore P, Younkin SG, Berr C, Gudnason V, Rujescu D, Dickson DW, Dartigues J-F, DeStefano AL, Ortega-Cubero S, Hakonarson H, Campion D, Boada M, Kauwe JK, Farrer LA, Van Broeckhoven C, Ikram MA, Jones L, Haines JL, Tzourio C, Launer LJ, Escott-Price V, Mayeux R, Deleuze J-F, Amin N, Holmans PA, Pericak-Vance MA, Amouyel P, van Duijn CM, Ramirez A, Wang L-S, Lambert J-C, Seshadri S, Williams J, Schellenberg GD (2017) Rare coding variants in PLCG2, ABI3, and TREM2 implicate microglial-mediated innate immunity in Alzheimer’s disease. Nat Genet 49:1373–1384. doi: 10.1038/ng.3916
Spangenberg E, Severson PL, Hohsfield LA, Crapser J, Zhang J, Burton EA, Zhang Y, Spevak W, Lin J, Phan NY, Habets G, Rymar A, Tsang G, Walters J, Nespi M, Singh P, Broome S, Ibrahim P, Zhang C, Bollag G, West BL, Green KN (2019) Sustained microglial depletion with CSF1R inhibitor impairs parenchymal plaque development in an Alzheimer’s disease model. Nat Commun 10:3758. doi: 10.1038/s41467-019-11674-z
Ståhl PL, Salmén F, Vickovic S, Lundmark A, Navarro JF, Magnusson J, Giacomello S, Asp M, Westholm JO, Huss M, Mollbrink A, Linnarsson S, Codeluppi S, Borg Å, Pontén F, Costea PI, Sahlén P, Mulder J, Bergmann O, Lundeberg J, Frisén J (2016) Visualization and analysis of gene expression in tissue sections by spatial transcriptomics. Science 353:78–82. doi: 10.1126/science.aaf2403
Stoltzner SE, Grenfell TJ, Mori C, Wisniewski KE, Wisniewski TM, Selkoe DJ, Lemere CA (2000) Temporal Accrual of Complement Proteins in Amyloid Plaques in Down’s Syndrome with Alzheimer’s Disease. The American Journal of Pathology 156:489–499. doi: 10.1016/S0002-9440(10)64753-0
Su W, Saravia J, Risch I, Rankin S, Guy C, Chapman NM, Shi H, Sun Y, Kc A, Li W, Huang H, Lim SA, Hu H, Wang Y, Liu D, Jiao Y, Chen P-C, Soliman H, Yan K-K, Zhang J, Vogel P, Liu X, Serrano GE, Beach TG, Yu J, Peng J, Chi H (2023) CXCR6 orchestrates brain CD8+ T cell residency and limits mouse Alzheimer’s disease pathology. Nat Immunol 24:1735–1747. doi: 10.1038/s41590-023-01604-z
Sudwarts A, Thinakaran G (2023) Alzheimer’s genes in microglia: a risk worth investigating. Mol Neurodegeneration 18:90. doi: 10.1186/s13024-023-00679-4
Sun N, Victor MB, Park YP, Xiong X, Scannail AN, Leary N, Prosper S, Viswanathan S, Luna X, Boix CA, James BT, Tanigawa Y, Galani K, Mathys H, Jiang X, Ng AP, Bennett DA, Tsai L-H, Kellis M (2023) Human microglial state dynamics in Alzheimer’s disease progression. Cell 186:4386-4403.e29. doi: 10.1016/j.cell.2023.08.037
Trias E, Beilby PR, Kovacs M, Ibarburu S, Varela V, Barreto-Núñez R, Bradford SC, Beckman JS, Barbeito L (2019) Emergence of Microglia Bearing Senescence Markers During Paralysis Progression in a Rat Model of Inherited ALS. Front Aging Neurosci 11:42. doi: 10.3389/fnagi.2019.00042
Tröscher AR, Wimmer I, Quemada-Garrido L, Köck U, Gessl D, Verberk SGS, Martin B, Lassmann H, Bien CG, Bauer J (2019) Microglial nodules provide the environment for pathogenic T cells in human encephalitis. Acta Neuropathol 137:619–635. doi: 10.1007/s00401-019-01958-5
Tsering W, Prokop S (2023) Neuritic Plaques — Gateways to Understanding Alzheimer’s Disease. Mol Neurobiol. doi: 10.1007/s12035-023-03736-7
Twohig D, Nielsen HM (2019) α-synuclein in the pathophysiology of Alzheimer’s disease. Mol Neurodegeneration 14:23. doi: 10.1186/s13024-019-0320-x
Venegas C, Kumar S, Franklin BS, Dierkes T, Brinkschulte R, Tejera D, Vieira-Saecker A, Schwartz S, Santarelli F, Kummer MP, Griep A, Gelpi E, Beilharz M, Riedel D, Golenbock DT, Geyer M, Walter J, Latz E, Heneka MT (2017) Microglia-derived ASC specks cross-seed amyloid-β in Alzheimer’s disease. Nature 552:355–361. doi: 10.1038/nature25158
Verkerke M, Berdenis Van Berlekom A, Donega V, Vonk D, Sluijs JA, Butt NF, Kistemaker L, De Witte LD, Pasterkamp RJ, Middeldorp J, Hol EM (2024) Transcriptomic and morphological maturation of human astrocytes in cerebral organoids. Glia 72:362–374. doi: 10.1002/glia.24479
Vilella A, Bodria M, Papotti B, Zanotti I, Zimetti F, Remaggi G, Elviri L, Potì F, Ferri N, Lupo MG, Panighel G, Daini E, Vandini E, Zoli M, Giuliani D, Bernini F (2024) PCSK9 ablation attenuates Aβ pathology, neuroinflammation and cognitive dysfunctions in 5XFAD mice. Brain, Behavior, and Immunity 115:517–534. doi: 10.1016/j.bbi.2023.11.008
Wang C, Zong S, Cui X, Wang X, Wu S, Wang L, Liu Y, Lu Z (2023) The effects of microglia-associated neuroinflammation on Alzheimer’s disease. Front Immunol 14:1117172. doi: 10.3389/fimmu.2023.1117172
Wang L, Hou H, Zi D, Habib A, Tan J, Sawmiller D (2019) Novel apoE receptor mimetics reduce LPS-induced microglial inflammation. Am J Transl Res 11:5076–5085
Wang Y, Fang D, Yang Q, You J, Wang L, Wu J, Zeng M, Luo M (2023) Interactions between PCSK9 and NLRP3 inflammasome signaling in atherosclerosis. Front Immunol 14:1126823. doi: 10.3389/fimmu.2023.1126823
Wang Y, Ulland TK, Ulrich JD, Song W, Tzaferis JA, Hole JT, Yuan P, Mahan TE, Shi Y, Gilfillan S, Cella M, Grutzendler J, DeMattos RB, Cirrito JR, Holtzman DM, Colonna M (2016) TREM2-mediated early microglial response limits diffusion and toxicity of amyloid plaques. Journal of Experimental Medicine 213:667–675. doi: 10.1084/jem.20151948
Wyss-Coray T, Yan F, Lin AH-T, Lambris JD, Alexander JJ, Quigg RJ, Masliah E (2002) Prominent neurodegeneration and increased plaque formation in complement-inhibited Alzheimer’s mice. Proc Natl Acad Sci USA 99:10837–10842. doi: 10.1073/pnas.162350199
Yang X, Wen J, Yang H, Jones IR, Zhu X, Liu W, Li B, Clelland CD, Luo W, Wong MY, Ren X, Cui X, Song M, Liu H, Chen C, Eng N, Ravichandran M, Sun Y, Lee D, Van Buren E, Jiang M-Z, Chan CSY, Ye CJ, Perera RM, Gan L, Li Y, Shen Y (2023) Functional characterization of Alzheimer’s disease genetic variants in microglia. Nat Genet 55:1735–1744. doi: 10.1038/s41588-023-01506-8
Yu G, Wang L-G, Han Y, He Q-Y (2012) clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters. OMICS: A Journal of Integrative Biology 16:284–287. doi: 10.1089/omi.2011.0118
Yuan P, Condello C, Keene CD, Wang Y, Bird TD, Paul SM, Luo W, Colonna M, Baddeley D, Grutzendler J (2016) TREM2 Haplodeficiency in Mice and Humans Impairs the Microglia Barrier Function Leading to Decreased Amyloid Compaction and Severe Axonal Dystrophy. Neuron 90:724–739. doi: 10.1016/j.neuron.2016.05.003
Zhao W, Wang X, Yin C, He M, Li S, Han Y (2019) Trajectories of the hippocampal subfields atrophy in the alzheimer’s disease: A structural imaging study. Frontiers in Neuroinformatics 13. doi: 10.3389/fninf.2019.00013

No competing interests reported.

Download PDF

Journal Publication

published 15 Feb, 2025

Read the published version in Acta Neuropathologica →

Editorial decision: Revision requested
28 Nov, 2024
Reviews received at journal
27 Nov, 2024
Reviews received at journal
22 Nov, 2024
Reviewers agreed at journal
07 Nov, 2024
Reviewers agreed at journal
07 Nov, 2024
Reviewers invited by journal
04 Nov, 2024
Editor assigned by journal
04 Nov, 2024
Submission checks completed at journal
04 Nov, 2024
First submitted to journal
04 Nov, 2024

You are reading this latest preprint version

Microglia aggregates define distinct immune and neurodegenerative niches in Alzheimer's disease hippocampus.

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials and methods

Results

Discussion

Conclusions

Limitations of the study

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1