Monocyte-derived Cells Invade Brain Parenchyma and Amyloid Plaques in Human Alzheimer’s Disease Hippocampus

Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 https://doi.org/10.1186/s40478‑023‑01530‑z RESEARCH © The Author(s) 2023. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creat iveco mmons. org/ licen ses/ by/4. 0/. The Creative Commons Public Domain Dedication waiver (http:// creat iveco mmons. org/ publi cdoma in/ zero/1. 0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Open Access Acta Neuropathologica Communications Monocyte‑derived cells invade brain parenchyma andamyloid plaques inhuman Alzheimer’s disease hippocampus Clara Muñoz‑Castro1,2,4†, Marina Mejias‑Ortega3,4†, Elisabeth Sanchez‑Mejias3,4, Victoria Navarro1,2,4, Laura Trujillo‑Estrada3,4, Sebastian Jimenez1,2,4, Juan Antonio Garcia‑Leon3,4, Juan Jose Fernandez‑Valenzuela3,4, Maria Virtudes Sanchez‑Mico1,2,4, Carmen Romero‑Molina1,2,4, Ines Moreno‑Gonzalez3,4, David Baglietto‑Vargas3,4, Marisa Vizuete1,2,4, Antonia Gutierrez3,4* and Javier Vitorica1,2,4* Abstract Microglia are brain‑resident myeloid cells and play a major role in the innate immune responses of the CNS and the pathogenesis of Alzheimer’s disease (AD). However, the contribution of nonparenchymal or brain‑infiltrated myeloid cells to disease progression remains to be demonstrated. Here, we show that monocyte‑derived cells (MDC) invade brain parenchyma in advanced stages of AD continuum using transcriptional analysis and immunohistochemical characterization in post‑mortem human hippocampus. Our findings demonstrated that a high proportion (60%) of demented Braak V–VI individuals was associated with up‑regulation of genes rarely expressed by microglial cells and abundant in monocytes, among which stands the membrane‑bound scavenger receptor for haptoglobin/hemo‑ globin complexes or Cd163. These Cd163‑positive MDC invaded the hippocampal parenchyma, acquired a microglial‑ like morphology, and were located in close proximity to blood vessels. Moreover, and most interesting, these invading monocytes infiltrated the nearby amyloid plaques contributing to plaque‑associated myeloid cell heterogeneity. However, in aged‑matched control individuals with hippocampal amyloid pathology, no signs of MDC brain infiltra‑ tion or plaque invasion were found. The previously reported microglial degeneration/dysfunction in AD hippocampus could be a key pathological factor inducing MDC recruitment. Our data suggest a clear association between MDC infiltration and endothelial activation which in turn may contribute to damage of the blood brain barrier integrity. The recruitment of monocytes could be a consequence rather than the cause of the severity of the disease. Whether monocyte infiltration is beneficial or detrimental to AD pathology remains to be fully elucidated. These findings open the opportunity to design targeted therapies, not only for microglia but also for the peripheral immune cell popula‑ tion to modulate amyloid pathology and provide a better understanding of the immunological mechanisms underly‑ ing the progression of AD. Keywords Alzheimer’s disease, Myeloid cells, Microglia, Brain infiltration, Human hippocampus, Amyloid plaques †Clara Muñoz‑Castro and Marina Mejias‑Ortega have contributed equally as co‑first authors *Correspondence: Antonia Gutierrez [email protected] Javier Vitorica [email protected] Full list of author information is available at the end of the article Page 2 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 Introduction Alzheimer’s disease (AD) is a life-threatening neurodegenerative condition that progressively impairs cognitive function and daily living skills. It is the most common pathologic cause of dementia in the late adult life, and no cure is currently available. AD is neuropathologically characterized by abnormal aggregation of the amyloid-β (Abeta) peptides in plaques and hyperphosphorylated neuronal tau protein in neurofibrillary tangles, synaptic damage, and neuronal loss, together with glial-mediated innate immune responses [14]. In the last years, genome-wide association studies (GWAS) have identified many AD-risk genes related to microglial function and brain innate immunity [8]. Microglia, the brain parenchymal-resident myeloid cells and main immune cell components of the central nervous system (CNS), play a key role in brain homeostasis and AD pathogenesis [35, 43]. Under physiological conditions, other cells of the myeloid linage, such as macrophages associated with the CNS border, localized in the choroid plexus, leptomeningeal and perivascular spaces, as well as circulating monocytes and lymphocytes, among others, constantly surveil the interface between blood or cerebrospinal fluid and brain parenchyma [6, 18, 26]. Indeed, inflammatory neurological diseases are frequently associated with disruption of the blood–brain barrier (BBB) and peripheral immune cell infiltration [32]. Typical examples are infiltration of lymphocytes and monocyte-derived cells (MDC) in multiple sclerosis and amyotrophic lateral sclerosis, which seem to play an important role in the development of these neurodegenerative conditions [3, 26, 42, 66]. In post-mortem brain tissue from individuals with AD, as well as AD transgenic mice, CD4+ and CD8+ T lymphocytes are present in both vascular endothelium and brain parenchyma [13, 32, 57]. However, to date it is unclear whether circulating monocytes can infiltrate AD brains and differentiate into microglial cells [42], due to the difficulty in differentiating brain-resident microglia from infiltrating myeloid populations as they show similar morphology and express mostly the same markers [4]. The microglial population self-renews autonomously without the contribution of bone marrow-derived peripheral myeloid cells under steady state conditions [49]. However, microglial dysfunction, including impaired repopulation capacity, as occurs in AD brains [35, 51], could trigger monocyte recruitment to populate brain tissue. In fact, microglial depletion strategies induced a rapid repopulation by peripherally-derived macrophages [9, 21, 31, 41, 44, 58]. The presence of peripheral-derived monocytes in AD brain parenchyma and assessing their contribution to AD pathology remain a topic of debate. In this study, using transcriptional analysis together with immunohistochemical characterization, we provided evidence of circulating monocyte infiltration into the brains of AD. Our results revealed a myeloid subpopulation cluster associated with monocyte-specific genes, but not with the classical microglial signature, in the hippocampus of Braak V–VI individuals. These cells were identified in brain parenchyma tissue by their distinct expression of the Cd163 marker, showed a microgliallike morphology, were located close to blood vessels, and invaded amyloid plaques. However, in aged-matched high pathology controls no parenchymal MDC were detected and plaques were devoid of Cd163-cells. We propose that MDC infiltration was associated with endothelial activation and AD pathology severity. The therapeutic value of targeting brain-resident myeloid cells and peripheral monocytes to treat AD needs to be explored. Materials andmethods Human brain samples Medial temporal lobe (hippocampal and parahippocampal regions) samples were obtained from the tissue bank for neurological research Fundación CIEN (BT-CIEN; Centro de Investigación de Enfermedades Neurologicas; Madrid, Spain), the Neurological Tissue Bank of IDIBELL-Hospital of Bellvitge (Barcelona, Spain), and the Neurological Tissue Bank BioBanco-Hospital clínico- IDIBAPS (Barcelona, Spain). This study was approved by the Portal de Ética de la Investigación Biomédica de Andalucía (PEIBA) de la Consejería de Salud from Andalucía (Spain), as well as by the corresponding biobank ethics committees and by the “Comite de Etica de la Investigacion (CEI), Hospital Virgen del Rocio,” Seville, Spain. Subjects were classified according to Braak stage. Demographic and pathological characteristics of these cases are described in Table1. Only Braak V–VI individuals (77.98 ± 12.48years) met the clinical and neuropathological criteria for AD. Braak II subjects (77.50 ± 8.81) are considered as non-demented age-matched controls in this study. Younger subjects (49.50 ± 5.95years) without any known psychiatric or neurological disease, and lack of neuropathological lesions, are referred as Braak 0. For biochemical characterization, unfixed −80 °C frozen samples were used. For morphological studies, human samples were fixed in cold 4% paraformaldehyde in 0.1M phosphate buffer (PB) for 24–48h, cryoprotected in sucrose, stored at −80°C, sectioned at 30μm thickness on a freezing microtome, and serially collected in wells containing 0.1M phosphate buffer saline (PBS) and 0.02% sodium azide. The anatomical boundaries of the hippocampal and parahippocampal regions were identified in Nissl-stained sections by their cytoarchitecture and location using the human brain atlas [16]. Page 3 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 The areas located between the stereotaxic coordinates of Bregma 9.3mm and 34.6mm were analyzed. RNA, DNA andprotein extraction Total RNA, DNA and proteins were extracted using TRIsure isolation reagent (Bioline) following the manufacturer recommendations. RNA was purified using the RNeasy Mini Kit (Quiagen) and RNA integrity (RIN; 5.66 ± 1.06) was determined using RNA Nano 6000 (Agilent). RNA and DNA were quantified using a NanoDrop 2000 spectrophotometer (Thermo Fischer). Retrotranscription (RT) was performed with the High Capacity cDNA Reverse Transcription Kit (Applied Biosystem) using 4μg of total RNA as template. Proteins were quantified using the Lowry method. Quantitative real‑time RT‑PCR The real-time RT-PCR reaction mixture was performed in a final volume of 20μl containing 40ng of cDNA, iTaq Universal Probes Supermix (Bio-Rad) (2X), and Taqman Gene Expression Assay (Applied Biosystem; Additional file1: TableS1) (20X). Amplification and measurement were performed by ABI PRISM Sequence Detection Systems 7900 (Applied Biosystems). The reaction was first incubated at 50° C for 2min, 95° C for 10min, and then 40 cycles at 95° C for 15s and 60° C for 1min. For the quantification of cDNA level, we used the comparative double delta cycle threshold (Ct) method (2−ΔΔCt) [30, 46], using GAPDH as the housekeeping gene and Braak II subjects as the reference group. The Ct values were calculated with software provided by Applied Biosystems (SDS 1.7). To validate GAPDH as a housekeeper gene, cDNA levels were also determined by β-actin, 18S, UBE2D2, CYC1, and RPL13. We observed a very significant linear correlation (p < 0.0001) among Ct of GAPDH and any other housekeeper genes (β-actin, r = 0.863; 18S, r = 0.565; UBE2D2, r = 0.880; CYC1, r = 0.945; and RPL13,r = 0.834). Thus, normalization using any of them produced identical results. Array microfluidic cards Taqman Array Microfluidic Cards (Thermo Fisher) were custom-made with the probes described in Additional file1: TableS2 and the manufacturer’s protocol was followed. Briefly, we loaded 100 μL of each samplespecific PCR reaction mix, composed of Taqman mastermix (Thermo Scientific) (2x), and cDNA at 10ng/μl. Eight samples per card, balanced for Braak stage, were run simultaneously. We vertically centrifuged the plate at 1200rpm for 2min on a Sorvall ST40R centrifuge (Thermo Scientific). After sealing, the plate was read in the ViiATM 7 Real-Time PCR System (Applied Biosystems). Similarly to real-time RT-PCR, the Ct comparison method was used to quantify cDNA levels. In this case, the amount of cDNA was normalized by the geometric mean of the four housekeeping genes (see Additional file1: TableS2). The expression of the different genes was referred to the average expression of the Braak II group (n = 16 different individuals). Hierarchical clustering andgene set score Clustering analysis was performed in an unsupervised manner, using the public domain programming environment and language R (version 3.6.0). After Z-score normalization, we confirmed an inherent cluster structure and genes were used to generate a heatmap and perform hierarchical clustering to classify genes or subjects with a similar profile. Clustering was performed with Ward´s linkage method using Manhattan distance. The gene set score was used to quantitatively calculate variations in expression of different markers, associated with the same cell type [15]. Concisely, the gene set score was calculated for each sample ’j’ using the value ’eij’, which is the normalized expression level of gene ’i’ in Table 1 Summary of the neuropathological samples used in this study Braak stage Age (years) Gender (%) APOE genotype (%) Postmortem delay (h) Male Female ε2ε3 ε3ε3 ε3ε4 ε4ε4 Unfixed frozen sampes Braak 0 (n = 8) 49.50 ± 5.95 62.50 37.50 0.00 66.67 0.00 33.33 7.19 ± 3.29 Braak II (n = 26) 77.50 ± 8.81 53.85 46.15 9.09 77.27 9.09 4.55 8.33 ± 5.05 Braak III–IV (n = 15) 79.42 ± 12.37 46.66 53.33 7.69 61.54 30.77 0.00 5.42 ± 4.80 Braak V–VI (n = 44) 77.98 ± 12.48 43.18 56.82 5.56 69.44 22.22 2.78 8.99 ± 4.55 Fixed samples Braak 0 (n = 1) 58 100.00 0.00 – – – –– 5.00 Braak II (n = 8) 80.86 ± 6.77 28.57 71.43 – – – – 10.67 ± 5.46 Braak V–VI (n = 14) 72.93 ± 14.06 64.29 35.71 14.29 28.57 57.14 0.00 7.65 ± 3.14 Page 4 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 sample ’j’. We calculate the center gene expression matrix "cij" with the following equation: c ij =eij − 1 ns je ij ; where "ns" is the number of samples. The gene set score for sample "j" (Sj) is defined as the average of the matrix “cij”: S j= 1 n g ic ij ; where "ng" is the number of genes which constitutes the cluster. Apolipoprotein E (APOE) genotyping For determining the APOE epsilon alleles ε2, ε3, and ε4, manufacturer protocol from Applied Biosystems TaqMan SNP Genotyping Assays C___3084793 and C____904973 (Thermo Fisher Scientific) was followed. C___3084793 and C____904973 identify the single-nucleotide polymorphisms (SNP) rs429358 and rs7412, respectively. Briefly, the T allele at rs429358 and the C allele at rs7412 indicate the ɛ3 allele, whereas the T allele at both SNPs identify the ɛ2 allele, and the C allele at both positions determine the ɛ4 allele. PCR amplification reactions were performed in a final 10µl volume containing 100ng genomic DNA, TaqmanTM Genotyping Master Mix (2X), and TaqmanTM SNP Genotyping Assay (50X). The reaction was incubated at 50 ◦ C for 2min, followed by 95°C for 10min, and 40 cycles at 95°C for 15s and 64°C (rs429358) or 61°C (rs7412) for 1min. The fluorescent signal generated by PCR amplification is detected by ABI PRISM Sequence Detection Systems 7900 (Applied Biosystems) and the allelic discrimination analysis is performed by ABI Prism 7900 SDS Software (Applied Biosystems). Immunostaining First, for general antigen retrieval, free-floating sections were heated at 80°C for 20min in 50mM citrate buffer pH 6.0 and then, sections were treated with 3% hydrogen peroxide/ 10% methanol in PBS pH 7.4 for 20min to inhibit endogenous peroxidase. Subsequently, nonspecific staining was avoided using 5% goat or horse serum (Sigma-Aldrich) in PBS. For single labeling light microscopy, sections were incubated with the primary antibody (anti-Iba1 or anti-Cd163, see antibodies list) for 24–72h at room temperature followed by the corresponding biotinylated secondary antibody for 1h at room temperature (1:500 dilution, Vector Laboratories). Subsequently, incubation with horseradish peroxidase conjugated with streptavidin was performed for 90min (1:2000 dilution, Sigma-Aldrich) was performed. Finally, the peroxidase reaction was visualized with 0.05% of 3–3′-diaminoben- zidine tetrahydrochloride (DAB, Sigma-Aldrich), 0.03% of nickel ammonium sulfate and 0.01% of hydrogen peroxide in PBS. For double immunoperoxidase staining (Iba1/Abeta, Cd163/Abeta, Cd45/Abeta, Cd163/laminin) the first peroxidase reaction was carried out with DAB solution containing nickel ammonium sulfate to get a dark blue end product while the second peroxidase reaction was developed with DAB only, obtaining a brown reaction end product. The sections of the control and AD brains were simultaneously assayed using the same batches of solutions to minimize variability in immunolabeling conditions and the specificity of the immune reactions was controlled by omitting primary antisera. For double and triple immunofluorescence labeling (Mrc1/Cd163, Cd163/Iba1/Abeta, Cd163/Tmem119/ Abeta or Iba1/Trem2/Cd163), sections were incubated sequentially with primary antibodies followed by the corresponding Alexa 405/488/568/Cy5™ secondary antibodies (1:1000 dilution, Invitrogen) and DAPI (Sigma– Aldrich; 1:1,000) staining. Z-stacks images were taken with a Leica TCS SP8 confocal microscope and 3D-ana- lyzed with IMARIS software (version 9.5.1, Bitplane Inc.) Primary antibodies list The following primary antibodies were used for immunolabeling experiments: anti-oligomeric/fibrillar Abeta rabbit polyclonal OC (1:5000, Merck Millipore); anti- Iba1 rabbit polyclonal (1:1000, Wako); anti-Iba1 goat polyclonal (1:1000, Abcam); anti-Cd45 rabbit polyclonal (1:1000 dilution, Abcam); anti-Cd163 mouse monoclonal (1:500, Novocastra); anti-Trem2 rabbit polyclonal (1:200, LS BIO); anti-Mrc1 rabbit polyclonal (1:1000, Sigma-Aldrich); anti-laminin rabbit polyclonal (1:500, Sigma-Aldrich); anti-Tmem119 rabbit polyclonal (1:500, Sigma-Aldrich). Quantitative image analyses Myeloid cell loading Was defined as the percentage of total hippocampal area immunolabeled for Iba1 or Cd163. Whole hippocampal sections from non-demented controls (Braak II; n = 8; 2 sections/ individual) and AD cases (Braak V–VI; n = 11–14; 2 sections/individual) stained with Iba1 or Cd163 were digitalized using a VS120 high-throughput virtual slide system (Olympus, Denmark) connected to Olympus BX61VS microscope with a high-resolution digital color camera (VC50 Olympus). Sections were scanned with 40 × objective (NA = 0.9) with a resolution of 0.175µm/pixel (VSI file format). A focus map, created by selecting multiple coordinates with the optimal Z position, was automatically generated across the entirety section using a software-based autofocus function in the color brightfield mode. Quality parameters were not modified at any step staying uniform in all samples: gain (white balance) R:1.87; G1.00; B1.78; brightness 0.00, contrast 1.00, gamma 1.00, sharpness 6.00, and saturation 0.00)The high quality virtual slides were viewed using the Olyvia 2.6 software (Olympus, Denmark) and images from the region of interest were extracted (10–15 Page 5 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 images/section; image size of 1879 × 872 pixels; pixel size = 72ppp). Quantitative analyses were performed using the Visilog 6.3 image analysis software (Noesis, France). Thus, the 24-bit color digital images were calibrated (scale bar of 200μm = 117 pixels; 0.585 pixels/ μm) and converted to 8-bit gray scale images. After delimiting the reference area (excluding tissue ruptures, large blood vessels or white matter areas), images were binarized using a threshold level mask that was fixed between a range of 170–180 of intensity and manually set for each image to ensure a reliable quantification. The Iba1-positive o CD163-positive coverage area was calculated as sum labeled area measured/sum total area analyzed) × 100. All slides sampled were taken over for the sums, and a single burden was computed for each individual. The mean and standard deviation (SD) of the corresponding loadings were determined using all available data. To make accurate comparison between study groups tissue samples and sections were simultaneously processed using standardized handling and immunostaining protocols. Spatial distribution ofmyeloid cells aroundvessels Whole-slide imaging of hippocampal sections from AD brains (n = 9 Braak V–VI individuals), which were double immunostained with Cd163 or Iba1 and OC antibodies, was acquired at 40 × magnification using the automated virtual microscopy system (Olympus VS120, Japan) as described above. Images were further operated on ImageJ 1.52p image analysis system (NIH, Behtesda, Maryland, USA). Blood vessels (n = 63) were manually outlined, and three concentric circles of fixed radius (85 μm) were delineated surrounding each vessel. Then, the area covered by CD163-positive cells, Iba1-positive cells or OC-positive amyloid plaques was quantified in the ringshaped regions bounded by the three concentric circles for each image, after applying a threshold level mask (a range of 170–180 of intensity values for Cd163 and Iba1, and 150–160 for OC, was used). Periplaque myeloid cell quantification High resolution confocal Z-stack images from AD hippocampal sections (n = 6 Braak V–VI individuals; 2 sections per case; 73 total plaques analyzed) with triple (Cd163/Iba1/Trem2) immunofluorescence staining were taken using a Leica TCS SP8 confocal microscopy (Leica Microsystems CMS GmbH,Wetzlar) with a 63X oil immersion objectiv lens. The number of periplaque myeloid cells single immunolabeled for Iba1, Cd163 or Trem2, as well double labeled for Iba1/Cd163 or Trem2/ Cd163, was counted using ImageJ software. Only cells with a clearly visible cell body (DAPI-positive) were quantified. To assess the colocalization of Iba1 and Cd163, as well as Trem2 and Cd163, single planes from the confocal image stacks were split into individual channels. Western Blot Western blots were performed as previously described [11, 51]. In brief, to detect monomeric Abeta a 16% SDS-Tris-Tricine-PAGE was performed. Proteins were transferred to PVDF membranes (Immobilon-P Transfer Membrane, Merck-Millipore) and incubated with mouse monoclonal anti-Abeta-N-terminal antibody (82E1, 1:2000, IBL) overnight at 4° C. For total and phosphorylated Tau, we performed a 4% to 20% SDS-Tris–Glycine-PAGE (Bio-Rad) and transferred to nitrocellulose membranes (Hybond-C extra, Amersham). Mouse monoclonal anti-Tau (Tau46, 1:1000, Cell Signaling), mouse monoclonal anti-phosphoTauSer202, Thr205 (AT8, 1:1000, Thermo Scientific), and mouse monoclonal anti-phos- phoTauThr212, Ser214 (AT100, Innogenetics, 1:1000) were used. The membranes were then incubated with a goat anti-mouse IgG, horseradish peroxidase (HRP)-linked antibody (1:10,000, Cell Signaling). Proteins were visualized using Pierce ECL 2 Western Blotting Substrate (Thermo Scientific) in the ChemiDocTM Touch Imaging System (Bio-rad) and quantified using Image Lab software (Bio-Rad). For normalization purposes, proteins were first estimated by Lowry and protein loading was corrected by GAPDH (rabbit monoclonal anti-GAPDH antibody; 14C10 1:10,000, Cell Signaling). The phospho- Tau proteins were referred to total Tau levels. Statistical analysis Statistical analysis was performed using GraphPad Prism 8 (Prism) or SPSS software (IBM® SPSS® Statistics, v25). The normality of the data was evaluated using the Kolmogorov–Smirnov test. Nonnormally distributed data were represented as individual dots or using violin plots with the median and interquartile range and individual subject values. Data were compared using the Mann– Whitney U test (for two groups comparisons), Kruskal– Wallis tests (more than two groups) or Friedman test (for repeated measures) both followed by Dunn’s post hoc test. Linear correlations were analyzed using Spearman correlation. The Chi-square test or Fisher’s exact test was used to analyze the relation between qualitative variables. The significance was established at 95% confidence. Results Transcriptional characterization ofaspecific myeloid CD163 cluster inthehippocampus ofAD brains We first analyzed, using Spearman regression analysis, the possible modifications in the expression of microglial/MDC genes in human hippocampal samples, Page 6 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 classified by Braak stage (from Braak 0 to Braak VI, Fig.1a and Additional file1: Fig.S1a). Our data showed a highly heterogeneous response. In fact, we did observe a significant positive (increase), negative (decrease), and not significant correlation with the progression of the pathology (Fig.1a). Furthermore, compared directly with the age-matched non-demented Braak II samples, only a limited number of genes showed significant modifications in the Braak V–VI stage (Mann–Whitney test, p < 0.05) (Fig.1b). Next, we performed a hierarchical cluster analysis (Additional file 1: Fig. S1b) of genes that change significantly in Braak V–VI samples (Fig. 1b) and identified three main myeloid clusters; homeostatic microglia (hMG) (P2RY12, CX3CR1), activated microglia (aMG) (PU.1, CSF1, CD45, CD74) [15, 24, 35, 50]; and a third group including non-classic microglial markers [5, 12] which we termed Cd163 cluster (CD163, IFITM3, CCR2, CD68, and MSR1). Next, using the gene set score (Sj) [15], we analyzed whether the gene expression of these clusters was affected by the progression of AD. As shown in Fig.1c, a significant down-regulation in the expression of hMG genes was produced in the Braak V–VI group together with limited and heterogeneous aMG signature genes (Kruskal–Wallis, p = 0.006, Dunn test p = 0.006760 in Braak V–VI compared to Braak II, Fig.1d). These data were consistent with the weak microglial response in the human hippocampus previously described by our group [35, 51]. In fact, the expression of “classic” microglial markers (such as IBA1, CD11B and TREM2) (Fig.1f) did not show significant changes in Braak V–VI subjects. In contrast, we observed a clear and highly significant upregulation of the Cd163 cluster (Fig.1e, Kruskal–Wallis, p = 9.847e−6; post hoc Dunn test, p < 0.01, Braak V–VI compared to Braak 0 and II or Braak III-IV versus Braak 0) in this cohort. Therefore, there was a clear mismatch between the Cd163 cluster genes in the AD group compared to the active or classical microglial markers in the same sample cohort. Parenchymal andplaque‑associated CD163 cells represent apathological feature ofAD hippocampus Next, we analyzed by immunohistochemistry the existence of Cd163 positive cells in the hippocampus of AD (Braak V–VI) hippocampus, compared to control samples (non-demented individuals of Braak II individuals) samples. In parallel, we also analyzed the microglial cell population using the classical Iba1 labeling. As shown in Fig.2 (a1–5), in control samples Cd163-positive cells were restricted to blood vessels. In fact, control samples showed a very low relative abundance of Cd163 positive cells compared to Iba1 cells (0.97 ± 0.81% Cd163 load vs 15.50 ± 4.08% Iba1 load, n = 8, Fig. 2c). These data were consistent with the already known expression of CD163 in perivascular macrophages (see below) and with the low expression of CD163 detected by qPCR in these samples (see Fig.1e). However, in the hippocampus of Braak V–VI subjects (Fig.2a6–10), we also observed Cd163 positive cells located in blood vessels, but much more relevant and opposed to control samples, we also observed Cd163 positive cells present in the hippocampal parenchyma. These parenchymal Cd163 cells showed a ramified morphology and were unevenly distributed throughout the hippocampus, and in many cases a close association with blood vessels was clearly seen (Fig.2a8 and 10). Interestingly, the density and distribution pattern of Cd163+ cells was completely different from that observed for Iba1 microglial cells (Fig. 2b). Iba1 cells were much more abundant than Cd163 cells (Fig.2), and showed an even distribution throughout the parenchyma. This homogeneous location of Iba1 cells was better seen in control cases (Fig.2b1–5), since in the hippocampus of AD (Fig.2b6–10) microglia undergo profound changes, including activation and clustering around amyloid plaques, and in some regions as dentate gyrus degeneration with dystrophic morphology and the presence of nude areas of Iba1 cells, as previously reported by our group [51]. We confirmed our observations by quantitatively determining the relative abundance of Cd163 positive cells in hippocampal tissue sections. As shown (Fig.2c1), there was a clear and highly significant increase (0.97 ± 0.81% vs 2.30 ± 0.94%; Mann Whitney test p = 0.002159; n = 8 or n = 12 for Braak II and Braak V–VI samples, respectively) in the parenchymal area covered by Cd163 positive cells in Braak V–VI samples. However, no significant differences were detected in the Iba1 positive area (Fig.2c2). It should be noted that despite the clear increase in Braak V–VI samples, Cd163 positive cells still constituted a minor population compared to microglial cells (2.30 ± 0.94% vs 15.89 ± 7.03% for Cd163 and Iba1 cells, respectively). Next, we studied and compared the association of Cd163-positive cells and microglial Iba1 cells with Abeta plaques in early (Braak II control individuals with amyloid plaques, CERAD B) and advanced (demented Braak V–VI AD cases, CERAD C) stages of the pathology (Fig.3). As shown (Fig.3a1–4), despite the presence of relatively abundant Iba1 cells and Abeta plaques, no Cd163 positive cells were observed in the hippocampal parenchyma in Braak II cases. All Cd163 cells were restricted to blood vessels (Fig.3a2 and a4). Importantly, Iba1 or Cd45 positive and, consequently, activated microglial cells were clearly associated with Abeta plaques (Fig.3a5, a6). However, no Cd163 cells were associated with these plaques (see Fig.3a7). Page 7 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 Fig. 1 Expression analysis of microglial/myeloid genes in human post‑mortem hippocampal samples from Braak 0 to Braak V–VI (AD cases). The expression of the different microglial/myeloid genes was tested in human hippocampal samples classified by Braak stages, from Braak 0 (no pathology) to Braak VI (see Additional file 1: Fig. S1 for individual data). Braak stage dependent changes in expression were first analyzed using Spearman correlation analysis (a) and by Braak II vs Braak V–VI direct comparison using Mann Whitney test (b). c to e represented the Braak stage‑dependent variation of the gene set score of the three different clusters identified using data shown in b (Additional file 1: Fig. S1): homeostatic microglia (c), active microglia (d) and Cd163 cluster (e). (f) Expression of “classic” microglial markers (IBA1, CD11B and TREM2). The data were shown as violin plots including the individual cases. Significance, indicated in the figure, was tested using the Kruskal–Wallis test followed by the Dunn test Page 8 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 On the contrary, the presence of Cd163 cells was notable within Abeta plaques in the AD hippocampus (Fig.3a9, a11 and a14). These plaques also contained activated microglial cells with Iba1 (Fig.3a8, a10, and a12) and Cd45 (Fig.3a13) activated microglial cells. In fact, the presence of Cd163 positive cells was observed in 65.43 ± 14.84% (n = 7 different Braak V–VI individuals) of the Abeta plaques in this cohort. Together, these data demonstrated the presence of Cd163 positive cells in Braak V–VI plaques and also indicated that these cells were not a subset of microglia. In this sense, our immunohistochemical analysis pointed to a peripheral origin of plaque-associated Cd163 cells. As shown in Fig. 3b, Cd163-cells were preferentially located in blood vessels (perivascular macrophages), in the parenchyma nearby these vessels showing a ramified microglial-like morphology, as well as surrounding and infiltrating amyloid plaques located in the proximity of these vessels. Double Iba1/Cd163 immunofluorescence labeling showed that these Cd163-cells were mainly negative for Iba1 and were found in the brain parenchyma in close association with amyloid plaques and blood vessels (Fig.3b4–7). A subset ofplaque‑associated CD163 cells does notexpress classical microglial markers As demonstrated above, the presence of Cd163 cells in the hippocampal parenchyma and associated with amyloid plaques was a pathological feature of Braak V–VI samples. Furthermore, the distribution of Cd163 cells was completely different from the classic Iba1 pattern, and the close association with blood vessels indicated that these Cd163 positive cells in AD cases could, in fact, be infiltrated monocytes. To study this possibility, we analyzed the expression of ten “specific” genes predominantly expressed in monocytes, with low expression in microglia [59]. Indeed, using recent data from single cell RNA sequencing in the human cortex [38], we confirmed that these selected genes (Additional file 1: Table S2) were enriched in “cluster 10”, corresponding to myeloid cells, versus different subsets of microglia (not shown). Thus, we determined the expression of this particular set of genes using fluidic cards. Data from these experiments were shown as heatmap (Fig.4a1) or as gene-set-score (Fig.4a2). As shown, the expression of these genes was highly significant (Kruskal–Wallis test, p = 2.932e−5; Dunn post hoc test, p < 0.05) increased in Braak V–VI samples, compared to Braak 0 or Braak II (Fig.4a2), reinforcing the possible increase in myeloid infiltration. The expression of these genes was significantly correlated (Spearman, r = 0.7118 p < 0.0001, not shown) with the expression of genes included in the CD163-cluster defined in Fig.1e, supporting that both groups of genes may be expressed by the same cell population. On the other hand, the expression of classically defined microglial genes, determined in parallel, such as TREM2, AXL, CST7 or SALL1, showed a small increase in Braak V–VI samples (Fig. 4b1). Interestingly, while TREM2 expression was significantly correlated with ‘microglial’ genes, such as SALL1 and MEF2A (Additional file1: Fig.S2a, b), no correlation was observed for CD163-related genes (ie, CD163, IfITM2, IFITM3, F13A, or NRA1, Additional file1: Fig.S2a, c), indicating that TREM2 and CD163 may be, preferentially, expressed by different myeloid populations. Similarly, the expression of SALL1 (specific microglial transcription factor) significantly correlated with microglial genes but not with CD163-related genes (Additional file1: Fig.S2d). In parallel, using the same fluidic-cards, we have also analyzed the expression of two different genes (MRC1 and CD169) associated with perivascular macrophages [17, 23]. As shown, no change between groups was observed (Fig.4b2 and Additional file1: Fig.S3c). Furthermore, Mrc1 positive cells were restricted to the perivascular space and these cells were positive for both Cd163 and Mrc1 markers (Additional file1: Fig.S3a1, a2, b1–3), as expected from perivascular macrophages. It should be noted that the parenchymal Cd163-cells, present in Braak V–VI samples, were consistently negative for Mrc1 in all the analyzed cases (Additional file1: Fig.S3b4–6). Therefore, it is unlikely that the parenchymal Cd163 cells, enriched in Braak V–VI samples, were of perivascular macrophage origin. Fig. 2 Presence of Cd163‑positive cells in the hippocampal parenchyma of AD hippocampus. (a) Immunostaining for CD163 in Braak II (age‑matched controls, a1–5) and Braak V–VI (AD patients, a6–10) hippocampus. Cd163‑positive cells from Braak II individuals (a1) were limited to blood vessels in both DG (a2, boxed area a3) and CA1 (a4, boxed area a5) areas. AD samples (a6) showed Cd163 cells not only associated with blood vessels (purple arrows in a8 and a10), but also distributed throughout the hippocampal parenchyma (black arrows in a8 and a10). (b) Immunolabeling for Iba1 in the hippocampus of control (b1–5) and AD (b6–10) individuals. Iba1‑microglial cells from Braak II cases (b1) exhibited a homogenous distribution (b2 and b4, boxed areas b3 and b5) compared to AD hippocampus (b6) which included degeneration (b7, boxed area b8) and clustering (b9, boxed area b10). (c) Quantitative analysis of the parenchymal area (percentage) covered by Cd163 (c1) and Iba1 (c2) positive cells in control (n = 8) and AD (n = 12) samples. The results are shown as violin plots including the individual cases (dots). Mann–Whitney U test comparison between control and AD groups. BV: blood vessels; CA1: cornu ammonis; DG: dentate gyrus; g: granular layer; h: hilus; m: molecular layer; so: stratum oriens; sp: stratum pyramidale; sr: stratum radiatum; slm: stratum lacunosum‑moleculare.* Asterisk indicates Abeta plaque. Scale bars: a1, a6, b1 and b6, 1 mm; a2, a4, a7, a9, b2, b4, b7 and b9, 500 μm; a3, 50 μm; a5, a8, a10, b3, b5, b8 and b10, 20 μm (See figure on next page.) Page 9 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 To further explore the nature of the plaque-associated Cd163 population we performed triple immunofluorescence labelling (Cd163/Iba1/Abeta or Cd163/Iba1/ Trem2) in Braak V–VI hippocampal samples and combined with confocal microscopy and 3D-Imaris reconstruction (Fig.4c, d). Our data support the existence of Fig. 2 (See legend on previous page.) Page 16 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 On the other hand, TREM2 has been classically associated with activated microglial cells in AD [20, 24] and its expression is essential for the microglial response in AD and other neurodegenerative pathologies [62]. In fact, in the presence of Abeta accumulation, mutations or total ablation of TREM2 abolish both microglial migration and activation of the DAM phenotype [24, 60]. Therefore, if the identified CD163 cluster corresponded to activated microglial cells, a close correlation between the expression of these markers should be found. However, we did not find any significant correlation between CD163-clus- ter genes and microglial markers (Fig.5 and Additional file 1: Fig. S2), although TREM2 expression showed a highly significant correlation with SALL1 (a transcription factor expressed exclusively by microglia) [7]. Furthermore, IMARIS reconstruction of Abeta plaques or triple labeling experiments also confirm the presence of Cd163 positive and Iba1, Trem2 or Tmem119 negative cells. Thus, these data strongly support the existence of infiltrating monocytes within our AD population. Within the different markers analyzed, CD163 (a hemoglobin scavenger receptor) constitutes the best surface receptor to identify this peripheral myeloid population. Increase in the relative abundance of Cd163 positive cells, associated with different neurodegenerative processes, such as Parkinson’s disease [40], AD (this work and [10, 40], ischemic stroke [45] and even tumor-asso- ciated macrophages (glioblastoma) [59, 64] have been extensively reported. In this sense, two different recent works [17, 36] using snRNA-seq in controls and AD samples have also identified CD163 as a predominant altered marker in the AD cohort. In these two important works, CD163 expression has been preferentially associated with different microglial phenotypes, such as ARM [36] or in the final AD trajectory (AD1) [17]. Therefore, these data are apparently in contradiction to those reported in this work. However, our data also demonstrated the existence of two different CD163 populations, negative (10–18%) and positive for Iba1,Trem2 and/or Tmem119 (20–30% see Fig.4 and Additional file1: Fig.S4). Therefore, it is possible that this Cd163 and Iba1, Trem2 or Tmem119 positive population corresponds to ARM/AD1 cells, while the Cd163 positive /Iba1,Trem2 or Tmem119 negative cells were infiltrating monocytes. On the other hand, we cannot exclude that both populations were indeed infiltrated monocytes at different differentiation stages. In this sense, it has been proposed that the CNS environment could reprogram infiltrating monocytes towards a microglial phenotype [2] expressing most, but not all, “classical” microglial markers. On the other hand, the present results do not allow us to conclude whether monocyte infiltration is beneficial or detrimental to AD pathology. In fact, infiltrating monocytes have been postulated to show high phagocytic capacity [56]. Furthermore, Ccr2 ablation increases the Abeta pathology [25] and it has been demonstrated that enhanced recruitment of cerebral Abeta-associ- ated monocytes diminished neuropathology and preserved cognitive function in amyloidogenic models [28, 29]. Similarly, blocking PD-L1 signaling in AD models increased monocyte-derived cell infiltration into the cerebral parenchyma, reducing brain pathology [1, 48]. As a consequence, monocyte infiltration within the cerebral parenchyma might, to some extent, alleviate the pathology of AD. In this sense, our data also demonstrate the close association between Cd163 cells and Abeta plaques in AD samples. Furthermore, several markers associated with this population can participate in a phagocytic response, such as CD163, CD68 and MSR1 [4, 34, 55]. However, our data also showed that, in our AD population, the presence of infiltrating Cd163 positive cells is also correlated with lower levels of specific GABAergic interneuronal makers, such as PV and SOM. Based on our own data [46, 52], the decrease in the expression of GABAergic markers constitutes a “proxi” reflecting the neurodegenerative process observed in both AD cases and Abeta models. Consequently, a decrease in the expression of PV and SOM should reflect a reduction in GABAergic interneurons and, consequently, reflects a major neurological pathology. Therefore, although infiltrating monocytes could indeed have higher Abeta phagocytic capacity or a beneficial immune response, in our AD cohort, infiltration seems to be a consequence of a higher AD pathology (see below). In fact, we did not Fig. 6 CD163‑cells infiltration was associated with AD pathology and vascular endothelial activation. (a) Hierarchical clustering (Ward´s linkage method, Manhattan distance) analysis (a1) and gene set score (violin plots, a2) of Cd163 cluster, evaluated in the entire human cohort (n = 77 samples, comprising Braak stages 0 to VI stages). As shown, a minimal structure of two main clusters (cluster‑I and cluster‑II) was observed. The expression of Cd163‑genes was significantly higher in cluster‑II. (b) Using this minimal classification, we evaluated whether cluster‑II was enriched in advanced Braak stages (b1), dementia (b2) or the ApoE4 genotype (b3). As shown, cluster II was enriched in BraakV–VI samples (Chi‑square p < 0.0001) and, consequently, significantly associated (Fisher test p = 4.509e−005) with demented cases. No differences were observed with the ApoE4 genotype. (c) Since Cd163 infiltration (cluster II) was predominantly associated with Braak V–VI cases we next evaluated in this particular population the possible association with CCL2 (c1), CCR2 (c2) and CD3E (c3). As expected, we observed significant increased (Mann Whitney test; p < 0.05) in cluster II for all three genes tested. Furthermore, a similar analysis was performed testing the expression of vascular adhesion genes (c4). As shown, there was also a significant (Mann Whitney test, p < 0.05) increased levels in cluster II. (d) We also observed a significantly reduced expression of the GABAergic markers parvalbumin (PV) and somatostatin (SOM) in cluster II Braak V–VI individuals (See figure on next page.) Page 17 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 Fig. 6 (See legend on previous page.) Page 18 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 observe significant differences in tau accumulation or tau phosphorylation between AD samples with high or low Cd163 infiltration. In consonance with these data, it has been demonstrated the increase in the soluble form of Cd163 in cerebrospinal fluid (CSF) in late Parkinson’s disease (PD) cases that was associated with cognitive decline condition [37]. In fact, high levels of soluble Cd163 in CSF indicated a bad prognosis of PD [27]. Therefore, the monocyte infiltration seems to be preferentially associated with advanced stages of the neurodegenerative diseases. However, these data do not exclude the proposed beneficial role of monocyte infiltration in AD pathology, as previously reported [28, 29]. In fact, enhancing brain infiltrating monocytes (with high Abeta-removing capacity) in earlier stages of the pathology could indeed be a therapeutic approach to reduce Abeta accumulation in brains with AD. Finally, our data also indicate that monocyte infiltration is not the cause but a consequence of AD pathology. In fact, approximately 30% of the AD cases analyzed with high Abeta and Tau pathology had low or no myeloid infiltration. Furthermore, we also identified non-demented Braak II or Braak III–IV cases with high infiltration levels. Therefore, it is possible that other comorbidities, associated with aging or other pathologies (i.e. vascular pathology), could be responsible for the observed infiltration. In this sense, our data clearly suggest a clear relationship with endothelial activation and possible damage to the BBB. In fact, we observed an increase in the expression of intercellular adhesion genes in the Braak V–VI population, and, more importantly, the expression of these vascular adhesion genes increased in samples with high levels of CD163 expression. Thus, vascular endothelial activation, associated with AD or other pathologies, may be responsible for the recruitment and infiltration of inflammatory cells into the cerebral parenchyma. These data are consistent with the diminished integrity of the BBB and endothelial activation associated with cerebral amyloid angiopathy, vascular dementia, and AD [14, 39, 54] Furthermore, we have also recently reported the existence of microglial degeneration in specific areas (dentate gyrus) of approximately 50% of AD samples [51]. Since microglial depopulation also induces monocyte infiltration [9], microglial degeneration could also be a pathological factor that induces higher monocyte infiltration. Therefore, our data strongly suggest that monocyte infiltration is a consequence, not the cause, of AD and/or AD-associated pathology. In summary, our data strongly support the existence of a small proportion (over total microglial cells) of monocytes that infiltrate the AD brain parenchyma. The recruitment of monocytes could be a consequence rather than the cause of the severity of the disease. Whether invading monocyte-derived cells are detrimental or beneficial to disease progression remains elusive. Unrevealing this critical issue will open new opportunities for AD therapeutics. Supplementary Information The online version contains supplementary material available at https:// doi. org/ 10. 1186/ s40478‑ 023‑ 01530‑z. Additional file1: Additional Figures and Tables. Acknowledgements This study was supported by Instituto de Salud Carlos III (ISCiii) of Spain, co‑ financed by FEDER funds from European Union, through grants PI18/01556 and PI21/00914 (to JV) and PI18/01557 and PI21/00915 (to AG); by Junta de Andalucia Consejería de Economía y Conocimiento through grants US‑1262734 and P20‑00843 (to JV), UMA18‑FEDERJA‑211 (to AG) and PI18‑ RT‑2233 (to AG) co‑financed by Programa Operativo FEDER 2014‑2020; by Spanish Minister of Science and Innovation grant PID2019‑108911RA‑100 (to DBV), Beatriz Galindo Program BAGAL18/00052 (to DBV), grant PID2019‑107090RA‑I00 (to IMG) and Ramon y Cajal Program RYC‑2017‑21879 (to IMG); and by Malaga University grant B‑2019_06 (to ESM). CMC, MSV and CRM held a FPU (Formacion del Personal Universitario) predoctoral contracts from the Spanish Minister of Science and Innovation. MMO held a predoctoral contract from Malaga University and JJFV a postdoctoral contract from Junta de Andalucia. We would like to thank to all the patients and families that contributed to brain banks donation and make this work possible. Author contributions JV and AG designed, supervised and interpreted the study, and wrote the manuscript. CMC and MMO performed most of the experiments. MMO and ESM conducted the neuropathological examinations, performed the immu‑ nostainings, image analysis quantifications and figure artwork. CMC, VN and SJ conducted the molecular studies and bioinformatic analysis. LTE, JGL, JJFV, MSM and CRM were involved in data acquisition. MV, DBV and IMG assisted in data analysis. All authors revised and approved the manuscript. Clara Muñoz‑Castro and Marina Mejias‑Ortega shared the first authorship. Javier Vitorica and Antonia Gutierrez shared the senior authorship and correspond‑ ing authors. Availability of data and materials All study data have been provided in paper and supplementary materials. Declarations Competing interests The authors declare that there is no conflict of interest. Author details 1 Dpto. Bioquimica Y Biologia Molecular, Facultad de Farmacia, Universidad de Sevilla, C/ Prof. Garcia Gonzalez 2, 41012 Seville, Spain. 2 Instituto de Biome‑ dicina de Sevilla (IBiS)‑Hospital Universitario Virgen del Rocío/CSIC/Universidad de Sevilla, 41013 Seville, Spain. 3 Dpto. Biologia Celular, Genetica y Fisiologia, Instituto de Investigación Biomedica de Malaga‑IBIMA, Facultad de Ciencias, Universidad de Malaga, Campus de Teatinos S/N, 29071 Malaga, Spain. 4 Ce n‑ tro de Investigacion Biomedica en Red Sobre Enfermedades Neurodegenerati‑ vas (CIBERNED), 28031 Madrid, Spain. Received: 10 February 2023 Accepted: 14 February 2023 Page 19 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 References 1. Baruch K, Deczkowska A, Rosenzweig N, Tsitsou‑Kampeli A, Sharif AM, Matcovitch‑Natan O, Kertser A, David E, Amit I, Schwartz M (2016) PD‑1 immune checkpoint blockade reduces pathology and improves memory in mouse models of Alzheimer’s disease. Nat Med 22:135–137. https:// doi. org/ 10. 1038/ NM. 4022 2. Bennett ML, Bennett FC (2020) The influence of environment and origin on brain resident macrophages and implications for therapy. Nat Neuro‑ sci 23:157–166. https:// doi. org/ 10. 1038/ S41593‑ 019‑ 0545‑6 3. Bogie JFJ, Stinissen P, Hendriks JJA (2014) Macrophage subsets and micro‑ glia in multiple sclerosis. Acta Neuropathol 128:191–213. https:// doi. org/ 10. 1007/ S00401‑ 014‑ 1310‑2 4. Bonilla DL, Bhattacharya A, Sha Y, Xu Y, Xiang Q, Kan A, Jagannath C, Komatsu M, Eissa NT (2013) Autophagy regulates phagocytosis by modu‑ lating the expression of scavenger receptors. Immunity 39:537–547. https:// doi. org/ 10. 1016/J. IMMUNI. 2013. 08. 026 5. Böttcher C, Schlickeiser S, Sneeboer MAM, Kunkel D, Knop A, Paza E, Fidzinski P, Kraus L, Snijders GJL, Kahn RS, Schulz AR, Mei HE, Hol EM, Siegmund B, Glauben R, Spruth EJ, de Witte LD, Priller J (2019) Human microglia regional heterogeneity and phenotypes determined by mul‑ tiplexed single‑cell mass cytometry. Nat Neurosci 22:78–90. https:// doi. org/ 10. 1038/ S41593‑ 018‑ 0290‑2 6. Brezovakova V, Valachova B, Hanes J, Novak M, Jadhav S (2018) Dendritic cells as an alternate approach for treatment of neurodegenerative disorders. Cell Mol Neurobiol 38:1207–1214. https:// doi. org/ 10. 1007/ S10571‑ 018‑ 0598‑1 7. Buttgereit A, Lelios I, Yu X, Vrohlings M, Krakoski NR, Gautier EL, Nishi‑ nakamura R, Becher B, Greter M (2016) Sall1 is a transcriptional regulator defining microglia identity and function. Nat Immunol 17:1397–1406. https:// doi. org/ 10. 1038/ NI. 3585 8. Chen X, Holtzman DM (2022) Emerging roles of innate and adaptive immunity in Alzheimer’s disease. Immunity 55:1. https:// doi. org/ 10. 1016/J. IMMUNI. 2022. 10. 016 9. Cronk JC, Filiano AJ, Louveau A, Marin I, Marsh R, Ji E, Goldman DH, Smirnov I, Geraci N, Acton S, Overall CC, Kipnis J (2018) Peripherally derived macrophages can engraft the brain independent of irradiation and maintain an identity distinct from microglia. J Exp Med 215:1627– 1647. https:// doi. org/ 10. 1084/ JEM. 20180 247 10. Dal Bianco A, Bradl M, Frischer J, Kutzelnigg A, Jellinger K, Lassmann H (2008) Multiple sclerosis and Alzheimer’s disease. Ann Neurol 63:174–183. https:// doi. org/ 10. 1002/ ANA. 21240 11. Fernandez‑Valenzuela JJ, Sanchez‑Varo R, Muñoz‑Castro C, de Castro V, Sanchez‑Mejias E, Navarro V, Jimenez S, Nuñez‑Diaz C, Gomez‑Arboledas A, Moreno‑Gonzalez I, Vizuete M, Davila JC, Vitorica J, Gutierrez A (2020) Enhancing microtubule stabilization rescues cognitive deficits and ame‑ liorates pathological phenotype in an amyloidogenic Alzheimer’s disease model. Sci Rep 10:1. https:// doi. org/ 10. 1038/ S41598‑ 020‑ 71767‑4 12. Fernández Zapata C, Giacomello G, Spruth EJ, Middeldorp J, Gallaccio G, Dehlinger A, Dames C, Leman JKH, van Dijk RE, Meisel A, Schlickeiser S, Kunkel D, Hol EM, Paul F, Parr MK, Priller J, Böttcher C (2022) Differen‑ tial compartmentalization of myeloid cell phenotypes and responses towards the CNS in Alzheimer’s disease. Nat Commun 13:1. https:// doi. org/ 10. 1038/ S41467‑ 022‑ 34719‑2 13. Ferretti MT, Merlini M, Späni C, Gericke C, Schweizer N, Enzmann G, Engelhardt B, Kulic L, Suter T, Nitsch RM (2016) T‑cell brain infiltration and immature antigen‑presenting cells in transgenic models of Alzheimer’s disease‑like cerebral amyloidosis. Brain Behav Immun 54:211–225. https:// doi. org/ 10. 1016/J. BBI. 2016. 02. 009 14. Fisher RA, Miners JS, Love S (2022) Pathological changes within the cer‑ ebral vasculature in Alzheimer’s disease: New perspectives. Brain Pathol 32:1. https:// doi. org/ 10. 1111/ BPA. 13061 15. Friedman BA, Srinivasan K, Ayalon G, Meilandt WJ, Lin H, Huntley MA, Cao Y, Lee SH, Haddick PCG, Ngu H, Modrusan Z, Larson JL, Kaminker JS, van der Brug MP, Hansen D, v. (2018) Diverse brain myeloid expression profiles reveal distinct microglial activation states and aspects of Alzheimer’s disease not evident in mouse models. Cell Rep 22:832–847. https:// doi. org/ 10. 1016/J. CELREP. 2017. 12. 066 16. GAREY LJ, (1997) Atlas of the human brain. J Anat 191:477. https:// doi. org/ 10. 1046/J. 1469‑ 7580. 1997. 19130 4773.X 17. Gerrits E, Brouwer N, Kooistra SM, Woodbury ME, Vermeiren Y, Lambourne M, Mulder J, Kummer M, Möller T, Biber K, den Dunnen WFA, 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. https:// doi. org/ 10. 1007/ S00401‑ 021‑ 02263‑W 18. Greenhalgh AD, David S, Bennett FC (2020) Immune cell regulation of glia during CNS injury and disease. Nat Rev Neurosci 21:139–152. https:// doi. org/ 10. 1038/ S41583‑ 020‑ 0263‑9 19. Guedes JR, Lao T, Cardoso AL, El Khoury J (2018) Roles of microglial and monocyte chemokines and their receptors in regulating Alzheimer’s disease‑associated amyloid‑β and Tau pathologies. Front Neurol 9:1. https:// doi. org/ 10. 3389/ FNEUR. 2018. 00549 20. Hansen DV, Hanson JE, Sheng M (2018) Microglia in Alzheimer’s disease. J Cell Biol 217:459–472. https:// doi. org/ 10. 1083/ JCB. 20170 9069 21. Hohsfield LA, Najafi AR, Ghorbanian Y, Soni N, Hingco EE, Kim SJ, Jue AD, Swarup V, Inlay MA, Green KN (2020) Effects of long‑term and brain‑wide colonization of peripheral bone marrow‑derived myeloid cells in the CNS. J Neuroinflammation 17:279. https:// doi. org/ 10. 1186/ S12974‑ 020‑ 01931‑0 22. Jin F, Xi Y, Xie D, Wang Q (2022) Comprehensive analysis reveals a 5‑gene signature and immune cell infiltration in Alzheimer’s disease with qPCR validation. Front Genet 13. https:// doi. org/ 10. 3389/ FGENE. 2022. 913535 23. Jordão MJC, Sankowski R, Brendecke SM, Sagar LG, Tai YH, Tay TL, Schramm E, Armbruster S, Hagemeyer N, Groß O, Mai D, Çiçek Ö, Falk T, Kerschensteiner M, Grün D, Prinz M (2019) Single‑cell profiling identifies myeloid cell subsets with distinct fates during neuroinflammation. Sci‑ ence 363:1. https:// doi. org/ 10. 1126/ SCIEN CE. AAT75 54 24. 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. https:// doi. org/ 10. 1016/J. CELL. 2017. 05. 018 25. el Khoury J, Toft M, Hickman SE, Means TK, Terada K, Geula C, Luster AD (2007) Ccr2 deficiency impairs microglial accumulation and accelerates progression of Alzheimer‑like disease. Nat Med 13:432–438. https:// doi. org/ 10. 1038/ NM1555 26. Kierdorf K, Masuda T, Jordão MJC, Prinz M (2019) Macrophages at CNS interfaces: ontogeny and function in health and disease. Nat Rev Neuro‑ sci 20:547–562. https:// doi. org/ 10. 1038/ S41583‑ 019‑ 0201‑X 27. Konstantin Nissen S, Farmen K, Carstensen M, Schulte C, Goldeck D, Brockmann K, Romero‑Ramos M (2022) Changes in CD163+, CD11b+, and CCR2+ peripheral monocytes relate to Parkinson’s disease and cognition. Brain Behav Immun 101:182–193. https:// doi. org/ 10. 1016/J. BBI. 2022. 01. 005 28. Koronyo Y, Salumbides BC, Sheyn J, Pelissier L, Li S, Ljubimov V, Moyseyev M, Daley D, Fuchs DT, Pham M, Black KL, Rentsendorj A, Koronyo‑Hamaoui M (2015) Therapeutic effects of glatiramer acetate and grafted CD115+ monocytes in a mouse model of Alzheimer’s disease. Brain 138:2399– 2422. https:// doi. org/ 10. 1093/ BRAIN/ AWV150 29. Koronyo‑Hamaoui M, Sheyn J, Hayden EY, Li S, Fuchs DT, Regis GC, Lopes DHJ, Black KL, Bernstein KE, Teplow DB, Fuchs S, Koronyo Y, Rentsendorj A (2020) Peripherally derived angiotensin converting enzyme‑enhanced macrophages alleviate Alzheimer‑related disease. Brain 143:336–358. https:// doi. org/ 10. 1093/ BRAIN/ AWZ364 30. Livak KJ, Schmittgen TD (2001) Analysis of relative gene expression data using real‑time quantitative PCR and the 2(‑Delta Delta C(T)) Method. Methods 25:402–408. https:// doi. org/ 10. 1006/ METH. 2001. 1262 31. Lund H, Pieber M, Parsa R, Han J, Grommisch D, Ewing E, Kular L, Needhamsen M, Espinosa A, Nilsson E, Överby AK, Butovsky O, Jagodic M, Zhang XM, Harris RA (2018) Competitive repopulation of an empty microglial niche yields functionally distinct subsets of microglia‑like cells. Nat Commun 9:1. https:// doi. org/ 10. 1038/ S41467‑ 018‑ 07295‑7 32. 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. Neuro‑ degener Dis 18:49–56. https:// doi. org/ 10. 1159/ 00048 6200 33. Monoranu CM, Hartmann T, Strobel S, Heinsen H, Riederer P, DIstel L, Bohnert S (2021) Is there any evidence of monocytes involvement in Alzheimer’s disease? a pilot study on human postmortem brain. J Alzhei‑ mers Dis Rep 5:887–897. https:// doi. org/ 10. 3233/ ADR‑ 210052 34. Murray PJ, Wynn TA (2011) Protective and pathogenic functions of mac‑ rophage subsets. Nat Rev Immunol 11:723–737. https:// doi. org/ 10. 1038/ NRI30 73 Page 20 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 35. Navarro V, Sanchez‑Mejias E, Jimenez S, Muñoz‑Castro C, Sanchez‑Varo R, Davila JC, Vizuete M, Gutierrez A, Vitorica J (2018) Microglia in Alzheimer’s disease: activated, dysfunctional or degenerative. Front Aging Neurosci 10:1. https:// doi. org/ 10. 3389/ FNAGI. 2018. 00140 36. Nguyen AT, Wang K, Hu G, Wang X, Miao Z, Azevedo JA, Suh ER, van Deer‑ lin VM, Choi D, Roeder K, Li M, Lee EB (2020) APOE and TREM2 regulate amyloid‑responsive microglia in Alzheimer’s disease. Acta Neuropathol 140:477–493. https:// doi. org/ 10. 1007/ S00401‑ 020‑ 02200‑3 37. Nissen SK, Ferreira SA, Nielsen MC, Schulte C, Shrivastava K, Hennig D, Etzerodt A, Graversen JH, Berg D, Maetzler W, Panhelainen A, Møller HJ, Brockmann K, Romero‑Ramos M (2021) Soluble CD163 changes indicate monocyte association with cognitive deficits in Parkinson’s disease. Movement Disord 36:963–976. https:// doi. org/ 10. 1002/ MDS. 28424 38. Olah M, Menon V, Habib N, Taga MF, Ma Y, Yung CJ, Cimpean M, Khairal‑ lah A, Coronas‑Samano G, Sankowski R, Grün D, Kroshilina AA, Dionne D, Sarkis RA, Cosgrove GR, Helgager J, Golden JA, Pennell PB, Prinz M, Vonsattel JPG, Teich AF, Schneider JA, Bennett DA, Regev A, Elyaman W, Bradshaw EM, de Jager PL (2020) Single cell RNA sequencing of human microglia uncovers a subset associated with Alzheimer’s disease. Nat Commun 11:1. https:// doi. org/ 10. 1038/ S41467‑ 020‑ 19737‑2 39. Parodi‑Rullán RM, Javadov S, Fossati S (2021) Dissecting the crosstalk between endothelial mitochondrial damage, vascular inflammation, and neurodegeneration in cerebral amyloid angiopathy and Alzheimer’s disease. Cells 10:1. https:// doi. org/ 10. 3390/ CELLS 10112 903 40. Pey P, Pearce RKB, 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 2:1. https:// doi. org/ 10. 1186/ 2051‑ 5960‑2‑ 21 41. Plemel JR, Stratton JA, Michaels NJ, Rawji KS, Zhang E, Sinha S, Baaklini CS, Dong Y, Ho M, Thorburn K, Friedman TN, Jawad S, Silva C, Caprariello A, v., Hoghooghi V, Yue J, Jaffer A, Lee K, Kerr BJ, Midha R, Stys PK, Biernaskie J, Wee Yong V, (2020) Microglia response following acute demyelination is heterogeneous and limits infiltrating macrophage dispersion. Sci Adv 6:1. https:// doi. org/ 10. 1126/ SCIADV. AAY63 24 42. Prinz M, Priller J (2017) The role of peripheral immune cells in the CNS in steady state and disease. Nat Neurosci 20:136–144. https:// doi. org/ 10. 1038/ NN. 4475 43. Prinz M, Jung S, Priller J (2019) Microglia biology: one century of evolving concepts. Cell 179:292–311. https:// doi. org/ 10. 1016/J. CELL. 2019. 08. 053 44. Prokop S, Miller KR, Drost N, Handrick S, Mathur V, Luo J, Wegner A, Wyss‑Coray T, Heppner FL (2015) Impact of peripheral myeloid cells on amyloid‑β pathology in Alzheimer’s disease‑like mice. J Exp Med 212:1811–1818. https:// doi. org/ 10. 1084/ JEM. 20150 479 45. Rajan WD, Wojtas B, Gielniewski B, Miró‑Mur F, Pedragosa J, Zawadzka M, Pilanc P, Planas AM, Kaminska B (2020) Defining molecular identity and fates of CNS‑border associated macrophages after ischemic stroke in rodents and humans. Neurobiol Dis 137:1. https:// doi. org/ 10. 1016/J. NBD. 2019. 104722 46. Ramos B, Baglietto‑Vargas D, del Rio JC, Moreno‑Gonzalez I, Santa‑Maria C, Jimenez S, Caballero C, Lopez‑Tellez JF, Khan ZU, Ruano D, Gutierrez A, Vitorica J (2006) Early neuropathology of somatostatin/NPY GABAergic cells in the hippocampus of a PS1xAPP transgenic model of Alzheimer’s disease. Neurobiol Aging 27:1658–1672. https:// doi. org/ 10. 1016/J. NEURO BIOLA GING. 2005. 09. 022 47. Reed‑Geaghan EG, Croxford AL, Becher B, Landreth GE (2020) Plaque‑ associated myeloid cells derive from resident microglia in an Alzheimer’s disease model. J Exp Med 217. https:// doi. org/ 10. 1084/ JEM. 20191 374 48. Rosenzweig N, Dvir‑Szternfeld R, Tsitsou‑Kampeli A, Keren‑Shaul H, Ben‑Yehuda H, Weill‑Raynal P, Cahalon L, Kertser A, Baruch K, Amit I, Weiner A, Schwartz M (2019) PD‑1/PD‑L1 checkpoint blockade harnesses monocyte‑derived macrophages to combat cognitive impairment in a tauopathy mouse model. Nat Commun 10:1. https:// doi. org/ 10. 1038/ S41467‑ 019‑ 08352‑5 49. Rossi F, Lewis C (2018) Microglia’s heretical self‑renewal. Nat Neurosci 21:455–456. https:// doi. org/ 10. 1038/ S41593‑ 018‑ 0123‑3 50. Rustenhoven J, Smith AM, Smyth LC, Jansson D, Scotter EL, Swanson MEV, Aalderink M, Coppieters N, Narayan P, Handley R, Overall C, Park TIH, Schweder P, Heppner P, Curtis MA, Faull RLM, Dragunow M (2018) PU.1 regulates Alzheimer’s disease‑associated genes in primary human micro‑ glia. Mol Neurodegener 13:1. https:// doi. org/ 10. 1186/ S13024‑ 018‑ 0277‑1 51. Sanchez‑Mejias E, Navarro V, Jimenez S, Sanchez‑Mico M, Sanchez‑Varo R, Nuñez‑Diaz C, Trujillo‑Estrada L, Davila JC, Vizuete M, Gutierrez A, Vitorica J (2016) Soluble phospho‑tau from Alzheimer’s disease hippocampus drives microglial degeneration. Acta Neuropathol 132:897–916. https:// doi. org/ 10. 1007/ S00401‑ 016‑ 1630‑5 52. Sanchez‑Mejias E, Nuñez‑Diaz C, Sanchez‑Varo R, Gomez‑Arboledas A, Garcia‑Leon JA, Fernandez‑Valenzuela JJ, Mejias‑Ortega M, Trujillo‑Estrada L, Baglietto‑Vargas D, Moreno‑Gonzalez I, Davila JC, Vitorica J, Gutierrez A (2020) Distinct disease‑sensitive GABAergic neurons in the perirhinal cortex of Alzheimer’s mice and patients. Brain Pathol 30:345–363. https:// doi. org/ 10. 1111/ BPA. 12785 53. Silvin A, Uderhardt S, Piot C, da Mesquita S, Yang K, Geirsdottir L, Mulder K, Eyal D, Liu Z, Bridlance C, Thion MS, Zhang XM, Kong WT, Deloger M, Fontes V, Weiner A, Ee R, Dress R, Hang JW, Balachander A, Chakarov S, Malleret B, Dunsmore G, Cexus O, Chen J, Garel S, Dutertre CA, Amit I, Kipnis J, Ginhoux F (2022) Dual ontogeny of disease‑associated microglia and disease inflammatory macrophages in aging and neurodegenera‑ tion. Immunity 55:1448–1465.e6. https:// doi. org/ 10. 1016/J. IMMUNI. 2022. 07. 004 54. Situ M, Citalan‑Madrid AF, Stamatovic SM, Keep RF, Andjelkovic AV (2022) Transcriptomic profile of blood‑brain barrier remodeling in cerebral amy‑ loid angiopathy. Front Cell Neurosci 16:1. https:// doi. org/ 10. 3389/ FNCEL. 2022. 931247 55. Swanson MEV, Murray HC, Ryan B, Faull RLM, Dragunow M, Curtis MA (2020) Quantitative immunohistochemical analysis of myeloid cell marker expression in human cortex captures microglia heterogene‑ ity with anatomical context. Sci Rep 10:1. https:// doi. org/ 10. 1038/ S41598‑ 020‑ 68086‑Z 56. Thériault P, Elali A, Rivest S (2015) The dynamics of monocytes and microglia in Alzheimer’s disease. Alzheimers Res Ther 7:1. https:// doi. org/ 10. 1186/ S13195‑ 015‑ 0125‑2 57. Unger MS, Marschallinger J, Kaindl J, Klein B, Johnson M, Khundakar AA, Roßner S, Heneka MT, Couillard‑Despres S, Rockenstein E, Masliah E, Attems J, Aigner L (2018) Doublecortin expression in CD8+ T‑cells and microglia at sites of amyloid‑β plaques: A potential role in shaping plaque pathology? Alzheimers Dement 14:1022–1037. https:// doi. org/ 10. 1016/J. JALZ. 2018. 02. 017 58. Varvel NH, Grathwohl SA, Degenhardt K, Resch C, Bosch A, Jucker M, Neher JJ (2015) Replacement of brain‑resident myeloid cells does not alter cerebral amyloid‑β deposition in mouse models of Alzheimer’s disease. J Exp Med 212:1803–1809. https:// doi. org/ 10. 1084/ JEM. 20150 478 59. Venteicher AS, Tirosh I, Hebert C, Yizhak K, Neftel C, Filbin MG, Hovestadt V, Escalante LE, Shaw ML, Rodman C, Gillespie SM, Dionne D, Luo CC, Ravichandran H, Mylvaganam R, Mount C, Onozato ML, Nahed BV, Waki‑ moto H, Curry WT, Iafrate AJ, Rivera MN, Frosch MP, Golub TR, Brastianos PK, Getz G, Patel AP, Monje M, Cahill DP, Rozenblatt‑Rosen O, Louis DN, Bernstein BE, Regev A, Suvà ML (2017) Decoupling genetics, lineages, and microenvironment in IDH‑mutant gliomas by single‑cell RNA‑seq. Science 355:1. https:// doi. org/ 10. 1126/ SCIEN CE. AAI84 78 60. 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, Holtz‑ man DM, Colonna M (2016) TREM2‑mediated early microglial response limits diffusion and toxicity of amyloid plaques. J Exp Med 213:667–675. https:// doi. org/ 10. 1084/ JEM. 20151 948 61. Yan P, Kim KW, Xiao Q, Ma X, Czerniewski LR, Liu H, Rawnsley DR, Yan Y, Randolph GJ, Epelman S, Lee JM, Diwan A (2022) Peripheral monocyte‑ derived cells counter amyloid plaque pathogenesis in a mouse model of Alzheimer’s disease. J Clin Invest 132. https:// doi. org/ 10. 1172/ JCI15 2565 62. Yeh FL, Hansen DV, Sheng M (2017) TREM2, microglia, and neurodegen‑ erative diseases. Trends Mol Med 23:512–533. https:// doi. org/ 10. 1016/J. MOLMED. 2017. 03. 008 63. Zenaro E, Piacentino G, Constantin G (2017) The blood‑brain barrier in Alzheimer’s disease. Neurobiol Dis 107:41–56. https:// doi. org/ 10. 1016/J. NBD. 2016. 07. 007 64. Zhang H, Zhang N, Wu W, Wang Z, Dai Z, Liang X, Zhang L, Peng Y, Luo P, Zhang J, Liu Z, Cheng Q, Liu Z (2022) Pericyte mediates the infiltration, migration, and polarization of macrophages by CD163/MCAM axis in glioblastoma. iScience 25:1. https:// doi. org/ 10. 1016/J. ISCI. 2022. 104918 65. Zhang Y, Sloan SA, Clarke LE, Caneda C, Plaza CA, Blumenthal PD, Vogel H, Steinberg GK, Edwards MSB, Li G, Duncan JA, Cheshier SH, Shuer LM, Page 21 of 21 Muñoz‑Castroetal. Acta Neuropathologica Communications (2023) 11:31 • fast, convenient online submission • thorough peer review by experienced researchers in your field • rapid publication on acceptance • support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year • At BMC, research is always in progress. Learn more biomedcentral.com/submissions Ready to submit your research Ready to submit your research ? Choose BMC and benefit from: ? Choose BMC and benefit from: Chang EF, Grant GA, Gephart MGH, Barres BA (2016) Purification and characterization of progenitor and mature human astrocytes reveals transcriptional and functional differences with mouse. Neuron 89:37–53. https:// doi. org/ 10. 1016/J. NEURON. 2015. 11. 013 66. Zondler L, Müller K, Khalaji S, Bliederhäuser C, Ruf WP, Grozdanov V, Thie‑ mann M, Fundel‑Clemes K, Freischmidt A, Holzmann K, Strobel B, Weydt P, Witting A, Thal DR, Helferich AM, Hengerer B, Gottschalk KE, Hill O, Kluge M, Ludolph AC, Danzer KM, Weishaupt JH (2016) Peripheral monocytes are functionally altered and invade the CNS in ALS patients. Acta Neuro‑ pathol 132:391–411. https:// doi. org/ 10. 1007/ S00401‑ 016‑ 1548‑Y Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in pub‑ lished maps and institutional affiliations.