Glial-specific and inducible full body C3 deficiency does not affect amyloid pathology in the AppNL-G-F mouse model of Alzheimer’s disease

Glial-specific and inducible full body C3 deficiency does not affect amyloid pathology in the AppNL-G-F mouse model of Alzheimer’s disease | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Glial-specific and inducible full body C3 deficiency does not affect amyloid pathology in the AppNL-G-F mouse model of Alzheimer’s disease Pieter Dujardin, Joyce Foroozandeh, Roselien Verhaegen, Marlies Burgelman, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6597252/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background Alzheimer's disease (AD) is intricately linked with neuroinflammation, with the complement system, particularly C3, emerging as a critical player. However, research has been hampered by the reliance on classical germline C3 knockout and APP overexpressing mouse models, which do not allow to study temporal and cell-specific C3 effects, and do not accurately reflect the complexity of AD pathology. Methods In this study, we investigated the impact of conditional C3 deficiency on neuroinflammation and AD pathology, by generating microglia-specific (C3 mKO ), astrocyte-specific (C3 aKO ), and inducible full-body (C3 iKO ) C3 knockout mice. To assess the role of C3 in both acute and chronic neuroinflammation, we employed an intracerebroventricular (ICV) LPS injection model in these mice alongside studies in the App NL−G−F knock-in mouse model of AD upon aging and mild peripheral inflammation. Results Our results show that complement genes, including C3 , are upregulated in microglia and astrocytes from 40 weeks old App NL−G−F mice compared to their wildtype counterparts. Both microglia and astrocytes were shown to be significant sources of C3, as conditional C3 deficiency in either cell type led to decreased C3 expression and a dampened neuroinflammatory transcriptional response following ICV LPS injection. However, microglial- and astrocytic-specific C3 deficiency in App NL−G−F mice did not affect total hippocampal C3 protein levels and amyloid plaque burden upon both 40 weeks of aging and in mild peripheral inflammation conditions. Also full-body C3 knockout, induced at the age of 8 weeks, did not alter Aβ pathology and glial activation, despite the complete removal of C3. Conclusions Our findings show that while C3 contributes to neuroinflammatory responses, its role in chronic AD-associated pathology is more complex than previously thought. Our study using novel cell-specific and inducible C3 knockout mice combined with the knock-in App NL−G−F model provides new insights into the cell-specific roles of complement in AD, and highlights the need for further investigation into the complement system's involvement in neurodegenerative diseases. Alzheimer's disease complement C3 neuroinflammation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) plaques and tau neurofibrillary tangles, accompanied by widespread neuroinflammation, synaptic dysfunction, and neuronal loss 1 . As the leading cause of dementia worldwide, AD remains a significant public health challenge, with its prevalence expected to rise as populations age 2 . Although significant advancements in Aβ-targeting therapies have been made, including monoclonal antibodies such as lecanemab and aducanumab 3 , these approaches primarily target Aβ clearance, without addressing the underlying processes. Furthermore, these treatments have shown limited clinical efficacy with unfavorable risk/benefit profiles due to amyloid-related imaging abnormalities (ARIA) 4,5 . This underscores the growing need for fundamental research into upstream mechanisms of AD pathology, such as neuroinflammation 6,7 . Among the various immune pathways implicated, the complement system has emerged as a key mediator of neuro-inflammatory processes in AD, which may offer additional therapeutic targets 8,9 . The complement system is a tightly regulated cascade of over 30 soluble and membrane-bound proteins, traditionally associated with immune defense through its roles in opsonization, cell lysis, and recruitment of immune cells 10 . It can be activated through the classical, lectin and alternative pathways, that importantly all converge via the cleavage of the central component C3 into the biologically active fragments C3a and C3b. Beyond its classical functions in innate immunity, complement has essential roles in the central nervous system (CNS), most notably as a mediator of synaptic pruning during neuronal circuit refinement 11,12 . In the brain, C3 expression has been primarily attributed to glial cells, including microglia 13,14 and astrocytes 14–16 , and its activity is tightly regulated to prevent excessive inflammation. In AD, dysregulation of the complement system is thought to contribute to pathology, as exemplified by increased C3 levels in cerebrospinal fluid (CSF) and postmortem brain of AD patients 15,17–19 . Decades ago, it was discovered that Aβ can bind and activate complement 20–22 , aiding its clearance. In addition, complement binding to Aβ has also been shown to promote Aβ aggregation 23 , and excessive complement activity exacerbates synaptic pruning, microgliosis, and astrocytosis, contributing to neurodegeneration 15,18,24–28 . For example, C3 deficiency in aged plaque-rich APP/PS1 mice protects against synapse and neuron loss, decreases glial reactivity, and spares cognitive decline, despite an increased plaque burden 24 . However, these findings largely stem from studies using classical germline C3 knockout mice and APP-overexpressing AD models, which have significant limitations. Germline C3 knockouts fail to account for the temporal and cell-specific roles of C3, and first-generation AD transgenic mouse models artificially overexpress mutant APP potentially causing additional phenotypes that do not fully recapitulate the amyloid pathology observed in human AD 29 . Additionally, discrepancies between studies regarding C3’s effects have been attributed to the use of different Aβ-overexpression models, further complicating interpretations. These limitations highlight the need for more precise tools to dissect the role of C3 in AD. Finally, the mice used in these studies are typically housed under specific pathogen-free (SPF) conditions, which fail to replicate the inflammatory environment experienced by humans and may overlook critical interactions between systemic inflammation and disease progression 30,31 . To address these limitations, this study employs the App NL−G−F knock-in mouse model 32 of AD combined with novel microglia-specific (C3 mKO ), astrocyte-specific (C3 aKO ), and inducible full-body (C3 iKO ) C3 knockout mice, enabling glial specific and temporally controlled deletion of C3. Our study was performed in parallel with a similar study by Singh et al. (Lemere lab; unpublished results) in an accompanying paper. Moreover, we incorporated a previously introduced AD mouse model 30,31 in which we administer lipopolysaccharide (LPS) to 5 months old App NL−G−F mice to mimic multiple peripheral inflammatory episodes. Using these models, we investigated the impact of C3 deficiency in response to neuroinflammation as well as during AD pathology. As such, this study provides new insights into the nuanced roles of C3 in neuroinflammation, and highlights the importance of further research to untangle the dualistic nature of complement-mediated events in AD. METHODS Mice Mice were housed with 14- to 10-h light and dark cycles and received ad libitum food and water in individually ventilated cages under specific pathogen-free (SPF) conditions. Conditional C3 KO mice ( C3 fl/fl ; C57BL/6J background) were generated in house, as described below. The generation of App NL−G−F mice carrying Arctic, Swedish, and Beyreuther/Iberian mutations was described previously 32 . In all experiments age- and gender- matched littermates were used. All experiments complied with the current laws of Belgium (Law of 14. August 1986 related to protection and welfare of animals) and EU directive 2010/63/EU, and were approved by the animal ethics committee of Ghent University (EC 2021-003, 2023-001, 2024-035, 2024-036). Generation of C3 fl/fl mice Conditionally targeted embryonic stem (ES) cells (JM8A3.N1) containing the C3 tm1a knockout first allele with floxed exons 2–4 were obtained from the European Mouse Mutant Cell Repository (EUMMCR) 33 . ES cells were injected into C56BL/6J blastocysts which were transferred to pseudopregnant B6CBAF1 foster mothers. Resulting coat color chimeras were crossed with C56BL/6J females to check for germline transmission. Germline offspring containing the tm1a allele were crossed with Flpe deleter mice 34 to remove the LacZ reporter and the Neomycin selection marker to obtain the conditional tm1c allele. Genotyping of the C3 fl/fl mice was performed on crude DNA extracts from toe tissue samples. Primers used to genotype the mice are provided in Supplemental Table 1 . C3 fl/fl mice were crossed with Cx3Cr1 CreERT2/+ (B6.129(C)-Cx3cr1 tm2.1(cre/ERT2)Jung /Orl) 35 , Gfap Cre/+ (Tg(GFAP-cre)8Gtm) 36 or Rosa26 CreERT2 (B6.129- Gt(ROSA)26Sor tm1(cre/ERT2)Tyj /J) mice to generate tamoxifen inducible microglia-specific (C3 mKO ), astrocyte-specific (C3 aKO ), and tamoxifen inducible full-body (C3 iKO ) C3 knockout mice, respectively. Tamoxifen administration C3 mKO mice were subcutaneously injected with 20 mg/mL tamoxifen (Sigma-Aldrich; T5648) dissolved in corn oil (Sigma-Aldrich; C8267) twice, 2 days apart, at the age of 4 weeks, as described before 37,38 . For C3 iKO mice, animals were administered tamoxifen (50 mg/mL) dissolved in corn oil with 10% ethanol, via oral gavage, daily, for 5 days, at the age of 8 weeks, as determined via optimization to reach the highest possible induced knockout efficiency. Also C3 WT littermates received the same tamoxifen protocol to control for potential off-target effects. Intracerebroventricular injection ICV injections were performed as described before 39 . Mice were anesthetized with isoflurane and mounted on a stereotactic frame. A constant body temperature of 37°C was maintained using a heating pad. Injection coordinates were measured relative to the bregma intersection (anteroposterior − 0.7 mm, mediolateral + 1.0 mm, dorsoventral − 2.0 mm) and were determined using the Franklin and Paxinos mouse brain atlas. By using a Hamilton needle, 5 µL of either PBS or LPS from Salmonella enterica serotype abortus equi (Sigma-Aldrich; L-5886) (1 µg/mL) was injected into the left lateral ventricle. The subsequent sampling was performed 3 days after the ICV injections. Induction of mild peripheral inflammation LPS from Salmonella enterica serotype abortus equi (Sigma-Aldrich; L-5886) intraperitoneal (IP) injections (1.0 mg/kg body weight) were performed on day 0 and 7 at the age of 22 weeks, as previously described 30,31 . Mice were sacrificed two weeks after the second LPS injection at the age of 25 weeks. Body weight, temperature loss, and sickness behavior were checked daily for a week after each LPS injection. Tissue sample collection Mice were sedated through IP injection with an overdose of ketamine (87.5 mg/kg) and xylazine (12.5 mg/kg). After disappearance of paw and tail reflexes, mice were transcardially perfused using 10 mL 0.2% heparin (Sigma; H-3125) in ice-cold D‐phosphate-buffered saline (PBS) (Gibco; 14190‐094) per mouse (4.50 mL/min). For preparation of single cell suspensions, brains were carefully isolated from the skull and collected in 1.5 mL of ice‐cold 1× Hanks’ balanced salt solution (HBSS)-/- (Gibco; 14175/053). Samples were kept on ice and immediately processed. For all other analyses (immunohistochemistry, RNA/protein analysis), brains were carefully extracted from the skull and split into two hemispheres (mid-sagittal). From the left hemisphere, the hippocampus and cortex were micro-dissected, snap-frozen in liquid nitrogen and stored at -80°C until further use. The right hemisphere was fixed immediately in 4% PFA overnight (ON) for approximately 16 h at 4°C. Next, the right hemisphere was again split in half (mid-coronal). The right posterior half hemisphere was dehydrated and embedded in paraffin and stored at room temperature (RT) until further use. The right anterior half hemisphere was embedded in 5% 2-hydroxyethylagarose and stored at 4°C until further use. FACS of microglia and astrocytes from whole mouse brain For concurrent microglia and astrocyte isolation, the protocol was adapted from previous reports 40,41 . Brain samples were collected in ice-cold 1× HBSS-/-, and cut to pieces approximately 1 mm 3 in size using spring scissors. Brain slurry was dissociated into single cell suspensions using the Neural Tissue Dissociation Kit (P) (Miltenyi Biotec; 130-092‐628). Samples were always kept on ice unless stated otherwise. Cells were enzymatically dissociated using activated enzyme (P) for 15 min at 37°C and enzyme (A) for 2 × 10 min at 37°C under continuous nutation. Additionally, the samples were mechanically dissociated by trituration in between enzymatic dissociation steps. To stop the enzymatic reaction, samples were diluted with an excess of 1× HBSS-/-. The samples were then passed through a 70 µM cell strainer (BD Falcon; 734‐0003) and mixed with 90% Percoll™ (Merck; GE17‐5445‐02) PLUS equilibrated in HBSS−/− pH 7.4 and to obtain a final concentration of 24% Percoll™ PLUS. Next, the samples were spun down at 300g for 11 min at RT with a low acceleration and deceleration brake. The myelin layer and supernatant were aspirated and the pellet was resuspended in 50 µL of 0.5% bovine serum albumin (BSA) (Jackson ImmunoResearch; 001‐000‐162) in D‐PBS (Gibco; 14190‐094). Single cell suspensions were pre-incubated with Fc Block (1/100) (BD Biosciences; 553142) for 10 min at 4°C and stained with appropriate antibodies at 4°C in the dark for 30 min. Antibodies and dilutions are listed in Supplemental Table 2 . Reactions were stopped by adding an excess of staining buffer, cells were spun down at 400g for 7 min at 4°C. Pellets were resuspended in FACS buffer and transferred through a 35 µm mesh into a 5 mL Falco®Round‐Bottom Polystyrene Test Tube with Cell Strainer Snap Cap (Fisher Scientific; 08‐771‐23). Cell viability was assessed using DAPI 1/200, added immediately prior to sorting. Flow cytometry and cell sorting was performed on the FACSymphony S6 using the 85 µm nozzle. Cells were sorted into 2 mL Eppendorf tubes containing 450 µL RLT Plus lysis buffer (Qiagen) containing 1% β-mercaptoethanol. The samples were extensively vortexed and stored at -80°C until RNA isolation. RNA sequencing on isolated microglia and astrocytes RNA from sorted cells was isolated using the RNeasy Plus Micro Kit (Qiagen; 74034) according to the manufacturer’s instructions. The concentration and purity of the RNA was determined using the Nanodrop ND-1000 (Nanodrop Technologies, Thermo Scientific) and the Agilent 2100 Bio-Analyzer. After cDNA library preparation with the Illumina Stranded Total RNA prep (Illumina), sequencing was carried out on an Illumina NovaSeq 6000 instrument. Preprocessing of the RNA-seq data was performed by Trimmomatic v0.39 42 and quality control by FastQC v0.11.8 ( https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ ). Mapping to the reference mouse genome was accomplished by STAR v2.7.3a, BAM files were created with Samtools v1.9 and HTSeqCount v0.11.2 was used for counting 43,44 . The data was split up by cell type (microglia and astrocytes) and analyzed separately. EdgeR v3.32.1 was used to normalize both datasets 45 . One sample from the C57BL/6J control group was identified as an outlier in both datasets. This sample was removed from downstream analysis. Genes which did not meet the requirement of a count per million (cpm) larger than 1 in at least the number of samples equaling the smallest group size, 4 for both astrocytes and microglia, were filtered out. This resulted in an expression table containing 14141 genes and 9 samples for the astrocyte dataset and an expression table containing 12902 genes and 9 samples for the microglia dataset. A Limma-voom pipeline v3.46.0 was utilized to perform differential expression (DE) analysis 46 . Benjamini-Hochberg correction was used to adjust the p-values for multiple testing. To be labeled as a DE gene, a gene needed to have an adjusted p-value 0.5 or < -0.5. The R package pheatmap v1.0.12 ( https://CRAN.R-project.org/package=pheatmap ) was used to create a heatmap of the top 25 DE genes (according to adjusted p-value) between the WT C57BL/6J and App NL−G−F group for the astrocyte and microglia dataset respectively. Additionally, a heatmap was created with a selection of expressed complement genes (containing DEGs and non-DEGs) for both celltypes. In all heatmaps the displayed gene expression was log 2 normalized. The mean expression value per gene over all samples (per dataset) was calculated and then subtracted from each sample's particular gene expression value to scale the expression values. The R package EnhancedVolcano v1.20.0 ( https://github.com/kevinblighe/EnhancedVolcano ) was used to create volcano plots to visualize the results of the DE analyses between the WT C57BL/6J and App NL−G−F group for the astrocyte and microglia dataset respectively. The figures plot out the -log 10 adjusted p-value on the Y-axis versus the log 2 FC value on the X-axis for all genes in the respective expression table. Based on the utilized cut-offs (see above), genes were colored differently: red genes are significant DE genes, blue genes only meet the adjusted p-value cut-off, green genes only meet the log 2 FC cut-off and black genes don’t meet either requirement. A selection of complement genes was manually chosen and the expressed genes were labeled in the plot. To be able to compare the expression values of certain genes across datasets, a separate analysis was performed with astrocyte and microglia samples together. The same outliers were discarded as in the previous analysis. Instead of calculating log 2 CPM values and performing TMM (trimmed mean of M values) normalization as before, log 2 TPM values were calculated in this combined analysis. This takes into account the length of a gene and facilitates the comparison of expression values between different genes. Gene ontology (GO) enrichment analysis was performed using the clusterProfiler R package v4.10.0 47 . This was conducted on the DE gene sets of the DE analyses between the WT C57BL/6J and App NL−G−F group for the astrocyte and microglia dataset respectively. The full gene list of the respective expression tables was used as background for the enrichment analysis. All ontologies (“Biological Pathway”, “Molecular Function” and “Cellular Compartment”) were included and an adjusted p-value cut-off of 0.05 was utilized. The top 10 significantly enriched Biological Pathway (BP) GO categories were featured in a dot plot for each comparison. These top GO categories are ordered according to geneRatio which is the ratio of the input DE gene set annotated in the respective GO term. The adjusted p-value is displayed as the color of the dot and the size of the dot is determined by the Count parameter, which is the number of DE genes annotated in the respective GO term. Bone marrow-derived macrophage (BMDM) cultures BMDM cultures from C3 mKO and C3 WT littermates were generated as described previously 48,49 . Mice were euthanized in a CO 2 -chamber followed by dissection of femur and tibia bones. After dissection, the bones were briefly rinsed in 70% ethanol and cold D-PBS (Sigma: 14190-169) and femur and tibia of each limb were dislodged and opened at the knee side. Next, femur and tibia bones of one limb were combined into an 18-G perforated 0.5 mL eppendorf with the open knee side facing downwards. The 0.5 mL tube was placed into a 1.5 mL eppendorf and centrifuged for 1 min at 1900g (RT) to collect the bone marrow (BM) into the 1.5 mL collection tubes. Subsequently, the resulting BM pellets were resuspended in ACK lysis buffer (1 mL/pellet) (Lonza; 10-548E) and incubated at RT for 1 min, following filtration of the cell suspension over a 70 µm cell strainer (VWR International: 734-0003). Cell suspensions obtained from one mouse were pooled and spun for 5 min at 1500 rpm at 4°C to pellet the BM cells. After counting, the BM cells were resuspended in complete DMEM medium (Gibco: 41965-062) supplemented with 10% fetal calf serum (Gibco); 1X penicillin/streptomycin (Sigma: P4333); 1X non-essential amino acids (Lonza: BE13-114E), 0.4 mM sodium pyruvate (Sigma: S8636), 2 mM L-glutamine (Lonza; BE17-605F) and 20 ng/mL murine M-CSF (Protein Service Facility, VIB), and seeded into untreated 9 cm petridishes at 4 to 5 x 10 6 cells/dish. Cells were maintained in standard tissue culture conditions and the medium was replaced for 50% three days after seeding, with complete DMEM medium to which murine M-CSF (40 ng/mL) was freshly added. Six days after seeding, the cells were detached using accutase solution (Sigma; A-6964), counted and reseeded in 6-well plates at 1 x 10 6 cells/well. One day after reseeding, the BMDM cells were lysed with TRIzol reagent (500 µL/well) (Life Technologies Europe; 15596018) and stored at -80°C until RNA isolation. DNA extraction and deflox PCR DNA was extracted from cells of interest using the ISOLATE II Genomic DNA Kit (GC Biotech BV; BIO-52066) according to the manufacturer’s instructions. DNA concentration was measured using the Nanodrop ND-1000 (Nanodrop Technologies, Thermo Scientific). As input for the deflox polymerase chain reaction (PCR), 30 ng of DNA was mixed with the correct primers ( Supplemental Table 2 ) and GoTaq Green Master Mix (Promega). Next, amplified DNA samples were separated on a 2% agarose (Life Technologies; 15510-027) gel with 5 µL/100mL Midori Green Advance (NIPPON Genetics) for 40 min at 160 V and visualized with Gel Doc XR+ (Biorad). Real-time qPCR RNA was isolated from cortex using the Aurum Total RNA Mini Kit (Bio-Rad; 732–6820) according to the manufacturer’s instructions. RNA concentration was measured using the Nanodrop ND-1000 (Nanodrop Technologies, Thermo Scientific) and cDNA was prepared using the SensiFAST cDNA synthesis kit (Bioine; BIO-65054). Real-time quantitative polymerase chain reaction (RT-qPCR) was performed with the Light Cycler 480 system (Roche) using the SensiFAST SYBR No-ROX Kit (Bioline; BIO-98002). Volumes were dispensed using the I.DOT (DISPENDIX). Expression levels of the genes of interest were normalized to stable reference genes ( Gapdh , Hprt , Rpl , Ubc ), as determined by the geNorm Housekeeping Gene Selection Software 50 . Expression values were scaled as relative expression to the C3 WT control condition. The primer sequences of the forward and reverse primers for the different genes are provided in Supplemental Table 1 . C3 ELISA Hippocampal protein lysates were prepared in RIPA lysis buffer containing 50 mM Tris HCl (pH 8), 150 mM NaCl, 1% NP-40 (Tergitol), 0,5 mM EDTA (pH 8), and a Pierce Protease inhibitor tablet (Thermo Fisher Scientific; 88266) in PBS. Samples were homogenized with the Tissue Lyser II (Qiagen) at 20 Hz for 5 min, and debris was removed via centrifugation (14000g, 5 min, 4°C). The protein concentration of the supernatant was measured using the Pierce BCA protein assay (Thermo Fisher Scientific). A sandwich C3 ELISA was performed using the complement C3 mouse ELISA KIT (Abcam; ad157711), in accordance with the manufacturer’s protocol, loading 100 µg of protein lysate in the designed wells of the pre-coated antibody microtiter plate. The absorbance (450 nm) of each well was measured by VersaMax Microplate Reader (Molecular Devices) and the C3 concentration was determined using a non linear regression model in GraphPad Prism 10. Immunohistochemistry For paraffin sections, brains were cut into 5 µm slices (HM 340 E, Thermo Scientific), deparaffinized in xylene and ethanol, boiled in citrate buffer for 20 min, followed by blocking with 5% goat serum in PBS-T (PBS containing 0.3% Triton X-100) solution for 1 h at RT, and incubated with primary Abs in blocking buffer at 4°C ON. Primary antibody anti-GFAP (1/1000) (Dako; Z033429-2), and anti-6E10 (1/500) (BioLegend; 803001) were used. After washing with PBS, sections were stained with fluorophore-conjugated secondary Abs in PBS containing 0.1% Triton X-100 at RT for 1–1.5 h. Counterstaining was done with DAPI (1/1000). For vibratome sections, 50 µm sections were cut using the vibratome (Leica VT1200 S). Sections were preserved at 4°C in 0.01% sodium azide until staining. After washing the sections in 1x PBS, a peroxidase blocking was performed, 5% hydrogen peroxide (Sigma-Aldrich; H1009) in methanol. Sections were blocked using blocking buffer containing 0.2% Triton X-100, 0.5% BSA, and 5% goat serum for 1 hour at RT. Sections were incubated with primary antibody, rabbit anti-IBA1 (1/10000) (Bio Legend; 803001) ON at RT on a horizontal shaker. Next, sections were incubated with a secondary antibody, goat-anti-rabbit biotinylated-antibody (1/1000) (Thermo Scientific; 65-6140) at RT for 1 h, with Avidin-Biotin-Complex (ABC) reagent (Vector Laboratories; PK 6100) for 30 min in the dark at RT, and with 3,3’-diaminobenzide (DAB) (Vector Laboratories; SK-4105) for 2 min at RT. Finally, the sections were dehydrated in the Leica ST5010 Autostainer XL (bidi-90% EtOH-100% EtOH-xyleen) and mounted with medium (Entellan new). All immunostainings were imaged via confocal laser scanning microscopy Zeiss LSM780 or Zeiss Axioscan Z.1. 6E10 staining was quantified in QuPath 51 using pixel classification with Artificial neural network (ANN_MLP). GFAP staining was quantified using ImageJ software (version 1.53c, National Institutes of Health) via consecutive despeckle, gaussian blur, Yen tresholder and particle analyzer. IBA1 stainings were uploaded to the Aiforia® platform (Aiforia Inc., Cambridge, MA, USA) for analysis with custom deep learning algorithms that were developed in Aiforia 52 . Aβ extraction and ELISA Aβ was extracted and measured as described previously 30,53 . For the extraction, hippocampus samples were homogenized in Tissue Protein Extraction Buffer (Thermo Scientific) supplemented with complete protease inhibitor (Therma Scientific) and phosphatase inhibitor cocktail 2 and 3 (Sigma-Aldrich) using a Qiagen Tissue Lyser II (Qiagen, 5 min, 20 Hz). The beads in the homogenized samples were spin down for 5 min at 5000g at 4°C. Supernatant was collected and centrifuged at 4°C for 1 h at 100000g (TLA-100Rotor; Beckman Coulter). Supernatant containing the soluble Aβ fraction was removed and stored at -80°C. The pellet was further processed in GuHCl solution containing complete protease inhibitor, sonicated, vortexed, incubated for 60 min at 25°C and centrifuged at 70000g for 20 min at 4°C. Supernatant containing insoluble Aβ was 12 times diluted with GuHCl diluent (20 mM phosphate, 0.4 M NaCl, 2 mM EDTA, 10% Block Ace, 0.2% BSA, 0.05% NaN3, 0.075% CHAPS, protease inhibitor cocktail, pH 7.0) and immediately frozen at -80°C. To determine the Aβ 40 and Aβ 42 levels in soluble and insoluble protein extractions, 96-well immunoplates (Maxisorp Nunc; 430314) were coated ON at 4°C with anti- Aβ 40 (1.5µg/mL; JRF/cAb40/28) or anti- Aβ 42 antibody (1.5 µg/mL; JRF/cAb42/46) in coating buffer (10 mM Tris–HCl, 10 mM NaCl, 10 mM NaN 3 in 500 mL distilled H 2 O, pH 8.5). Plates were washed 5 times with PBST (PBS + 0.05% Tween-20), and residual protein binding sites were blocked for 4 h at RT with 100 µL blocking buffer (0.1% casein buffer). 30 µL of either standard (Aβ 1–42 ; rPeptide or Aβ 1–40 ; rPeptide) or sample was mixed with 30 µL detection antibody (JRF/ABN/25 coupled to HRPO (Janssen Pharmaceutica), 1:2,000, diluted in blocking buffer). After blocking, ELISA plates were washed 5 times with PBST and 50 µL of the standard/sample‐detection mixtures was added to the ELISA plates. Plates were incubated ON at 4°C, while slowly shaking. Absorption at 450 nm was measured after adding 50 µL TMB substrate (BD Biosciences OptEIA™) followed by stopping buffer (50 µL 1 M H 2 SO 4 ). The amount of Aβ was determined with GraphPad Prism 10 using a nonlinear regression model. Statistics A Limma-voom pipeline v3.46.0 was used to carry out DE analysis on the bulk RNA seq data. Benjamini-Hochberg correction was applied to correct the p-values for multiple testing. DE genes are genes with an adjusted p value 0.5 or < -0.5. All data are represented as mean ± standard deviation (SD). For comparison of two groups, unpaired student’s t-test was used. For comparison of multiple groups, significance was determined using two-way ANOVA with post-hoc Tukey’s multiple comparison test unless mentioned differently. All testing was two-sided. Differences were considered significant at p < 0.05. Significance levels are indicated on the graphs: *0.01 ≤ p < 0.05; **0.001 ≤ p < 0.01; ***0.0001 ≤ p < 0.001; and ****p < 0.0001. RESULTS Complement is upregulated in microglia and astrocytes in the App NL−G−F model Both microglia and astrocytes have been described as key producers of complement, including C3, in the brain of several neuroinflammatory disease models 13–16 . To examine the contribution of these glial cell types to complement-related gene expression in the App NL−G−F model of AD, we performed bulk RNA sequencing on isolated microglia and astrocytes from 40 weeks old App NL−G−F mice and age-matched C57BL/6J wild-type (WT) mice. Microglia were defined as CD45 low − int CD11b + Ly6C − cells and astrocytes as CD45 − CD11b − O1 − ACSA2 hi cells, as described previously 41 . To ensure purity of the sorted microglial population, Ly6C + cells were excluded to avoid contamination with pro-inflammatory microglia-like monocytes 54,55 . As expected, microglial marker genes ( Aif1 , Tmem119 , Itgam , P2ry12 ) were highly expressed in sorted microglia and virtually absent in the astrocytic fraction, whereas astrocytic markers ( Sox9, Aldh1l1, S100b, Gfap ) were highly expressed in sorted astrocytes and only barely detectable in microglia (Fig. 1 a). Marker genes of neurons ( Tubb3 , Dcx , Nefh ), oligodendrocytes ( Mog) , oligodendrocyte precursor cells ( Sox10 ), pericytes ( Slc6a12 ) and endothelial cells ( Mcam ) were barely detectable in both sorted populations, confirming the successful and selective enrichment of microglia and astrocytes using the selected strategy. In both glial cell types, gene ontology (GO) analysis of differentially expressed genes (DEGs) identified enrichment of inflammatory pathways, with the ‘inflammatory response’ and ‘immune effector process’ as top enriched biological pathways in microglia (Fig. 1 b) and astrocytes (Fig. 1 c), respectively. Within the microglial population, several genes associated with the disease-associated microglia (DAM) phenotype 56–58 , including Cst7 , Itgax , Clec7a , Csf1 , Axl , Lpl , and Spp1 , were among the top 25 DEGs (Fig. 1 f), confirming the presence of these cells in the App NL−G−F model and validating the reliability of our data. To further investigate the relative contribution of microglia and astrocytes to complement production in the App NL−G−F model, we then focused our analysis on complement-related genes. In microglia, Masp1 , C3 , and Cfb were significantly upregulated, whereas Cfp , Cd55 , and Cr2 were significantly downregulated in App NL−G−F mice compared to controls (Fig. 1 d, f). In astrocytes, the complement response appeared more robust, with multiple genes showing significant upregulation, including C1qa , C1qb , C1qc , C1ra , C1s1 , C2 , C4b , C3 , Hc , Cfb , Cfh , Cd55 , Cd59a , C3ar1 , Itgam , and Serping1 (Fig. 1 e, g). Among these, C1qa stood out as one of the highest upregulated DEGs in astrocytes (Fig. 1 g). Although C1qa , C1qb , and C1qc were also upregulated in microglia, their changes did not reach statistical significance. Despite this, microglia remained the predominant source of C1q, with log 2 (TPM) expression values more than double those observed in astrocytes ( Fig. S1 ), consistent with their established role as the primary producers of C1q in the brain. C3, the central effector protein of the complement cascade, was significantly upregulated in both microglia and astrocytes in the App NL−G−F model, with similar degrees of upregulation and comparable baseline expression between the two glial cell types ( Fig. S1 ). Microglial and astrocytic C3 are relevant neuroinflammatory factors To investigate the specific contributions of microglial and astrocytic C3 in neuroinflammation and AD pathology, we generated novel conditional C3 knockout mice using Cre(ERT2)-loxP technology from ES cells acquired via EUCOMM 33 ( Fig. S2a ). The insertion of the loxP sites did not affect C3 expression, as C3 mRNA levels in the liver and cortex from C3 fl/fl mice were comparable to those from WT mice ( Fig. S2b ). The microglia-specific C3 knockout (C3 mKO ) was generated by crossing C3 fl/fl mice with Cx3Cr1 CreERT2/+ mice, and astrocyte-specific C3 knockout (C3 aKO ) was generated by crossing C3 fl/fl mice with Gfap Cre/+ mice. CreERT2-mediated recombination in the C3 mKO line was induced by administration of tamoxifen at the age of 4 weeks. Specific C3 deletion in the C3mKO and C3aKO lines was validated by FACS and subsequent deflox PCR, and C3 mRNA staining via RNAScope, respectively. In C3 mKO mice, the Cre-mediated deletion of C3 was only present in microglia from C3 mKO mice, and not in C3 WT controls, nor in ‘non-microglial’ brain cells or BMDM cultures from C3 mKO mice ( Fig. S2c ). Similarly, in astrocytes, C3 deletion was validated to be specific in the astrocytes from C3 aKO mice. ( Fig. S2d ). To investigate the specific contributions of microglial and astrocytic C3 in neuroinflammation, we administered a low dose of LPS ICV to C3 mKO and C3 aKO mice and assessed how these glial-specific C3 deficiencies influence LPS-induced inflammation. First, we conducted a kinetics experiment in WT mice to compare neuroinflammatory gene expression prior to and 4 hours (h) and 3 days (d) after LPS injection. While the expression of proinflammatory genes Tnf , Il1β , Il6 , and Inos peaked at 4 h post injection (hpi), complement genes C3 , C1qa , C1r , and C1s were significantly upregulated at 3 dpi (Fig. 2 a), guiding our decision to focus on the 3 d timepoint for subsequent experiments. Remarkably, conditional C3 deletion in either microglia or astrocytes resulted in a significant 50% reduction in cortical C3 expression at 3 dpi (Fig. 2 b, c). Notably, the reduction in glial C3 expression also dampened the transcriptional responses of other proinflammatory genes. In C3 mKO mice, the expression of Tnf , Il1β , Inos , microglial activation marker Aif1 , and astrocytic activation marker Lcn2 was significantly decreased (Fig. 2 b). In C3 aKO mice, Il1β , Inos , and astrocytic activation markers Lcn2 and Serpina3a were significantly downregulated (Fig. 2 c). These findings highlight the importance of both microglial and astrocytic derived C3 in mediating neuroinflammatory responses, prompting its impact in the App NL−G−F model. Microglial and astrocytic C3 deficiency does not affect amyloidosis in the App NL−G−F model Next, we aimed to investigate the influence of microglial and astrocytic C3 deficiency on amyloid pathology. To this end, we backcrossed C3 mKO and C3 aKO mice into the App NL−G−F background. Mice were either aged to 40 weeks, or subjected to a mild peripheral inflammatory challenge via LPS injection at 22–23 weeks of age followed by analysis 2 weeks later as we described before 30 (Fig. 3 a, f). LPS administration induced an expected transient drop in body temperature and body weight and this systemic response was unaffected by either the App NL−G−F background or the C3 deficiency ( Fig. S3 ). Despite successful conditional deletion of C3 in microglia and astrocytes in C3 mKO (Fig. 3 a) and C3 aKO (Fig. 3 b) mice, respectively, C3 protein levels in the hippocampus remained unchanged in both aged and LPS-treated App NL−G−F ; C3 mKO and App NL−G−F ; C3 aKO mice compared to their App NL−G−F ; C3 WT littermates (Fig. 3 b, d, g, i). Moreover, we did not detect any difference in hippocampal C3 protein levels between App NL−G−F ; C3 WT mice and age-matched WT controls over all experimental setups. Importantly, amyloid pathology, assessed by 6E10 + Aβ plaque burden, remained unchanged across genotypes in both aged mice (Fig. 3 c, h) and mice subjected to mild peripheral inflammation (Fig. 3 e, j). These results indicate that while glial-derived C3 is involved in driving neuroinflammatory responses, its deletion does not significantly influence amyloidosis in the App NL−G−F model under either baseline ageing or mild peripheral inflammatory conditions. Inducible full-body C3 deficiency does not affect pathology in the App NL−G−F model The lack of impact of glial C3 deletion on amyloid pathology, together with our initial data showing that both microglia and astrocyte express C3, prompted us to next examine the impact of inducible full-body C3 deficiency (C3 iKO ). Thereto, we generated C3 iKO mice by crossing the C3 fl/fl with Rosa26 CreERT2/+ mice and subsequent backcrossing into the App NL−G−F background. The knockout in the generated App NL−G−F ; C3 iKO mice was induced at 8 weeks of age via oral gavage tamoxifen, administered daily for 5 days. This method was optimized by comparing IP and oral gavage administration of tamoxifen, with knockout efficiency assessed by deflox qPCR on gDNA from several organs and brain regions ( Fig. S4 ). We observed that oral gavage administration resulted in higher knockout efficiency in the CNS compared to IP injection ( Fig. S4c ). Additionally, while IP administration of tamoxifen led to the formation of lipogranulomas in the abdomen of some mice, likely due to excess oil used to dissolve the tamoxifen 59 , no such adverse effects were observed in the oral gavage group ( Fig. S4b ), further supporting our choice to use oral gavage as the preferred method. Also App NL−G−F ; C3 WT received tamoxifen to control for potential off-target effects. Resulting App NL−G−F ; C3 iKO and App NL−G−F ; C3 WT littermates were then again analyzed following ageing to 40 weeks (Fig. 4 a) or after mild peripheral inflammation (Fig. 4 f). The systemic response to the LPS administration was not affected by the C3 deficiency, as both App NL−G−F ; C3 WT and App NL−G−F ; C3 iKO mice displayed the expected drop in body temperature and body weight following LPS injection, with no significant differences between genotypes ( Fig. S5 ). ELISA analysis confirmed the complete removal of C3 in the hippocampus of C3 iKO mice, validating the effectiveness of our inducible knockout model (Fig. 4 b, g). Further analysis revealed that amyloid plaque burden remained unchanged between C3 iKO and C3 WT mice in both the ageing (Fig. 4 c) and mild peripheral inflammation (Fig. 4 h) conditions. Additionally, there were no significant differences in the levels of soluble or insoluble Aβ 1−40 and A β1−42 (Fig. 4 d, e, i, j), confirming that complete C3 deficiency did not impact amyloid deposition. No differences in microglial activation were observed in the hippocampus between 40 weeks old App NL−G−F ; C3 iKO and App NL−G−F ; C3 WT littermates, as assessed by IBA1 staining and quantification of microglial number, length, and circularity (Fig. 5 a–d). Similarly, qRT-PCR analysis of microglial activation markers ( Aif1 , Cd11b ) in the cortex revealed no significant differences between genotypes (Fig. 5 e, 6f). Astrocytic activation, assessed by GFAP staining in the hippocampus, also showed no differences between the groups (Fig. 5 g–h). Furthermore, qRT-PCR analysis of astrocytic activation markers ( Gfap , Aldh1l1 , Lcn2 , Serpina3a ) in the cortex did not reveal any differences between App NL−G−F ; C3 iKO and App NL−G−F ; C3 WT littermates (Fig. 5 i–l). These results show that, despite the complete removal of C3 at adult age, global C3 deficiency does not influence amyloidosis or neuroinflammation in the App NL−G−F AD model under the conditions tested. DISCUSSION Neuroinflammation and complement activation are increasingly recognized as key drivers of AD pathology, yet the relative contributions of microglia- and astrocyte-derived complement remain incompletely understood. In this study, we aimed to untangle the contributions of glial-derived complement to neuroinflammation and amyloidosis in AD, particularly in the context of App NL−G−F mice. To this end, we performed bulk RNA sequencing on FACS-isolated microglia and astrocytes from 40 week-old App NL−G−F mice and age-matched WT controls. Transcriptomic analyses revealed distinct gene expression profiles for both cell types, with inflammatory signaling pathways enriched in both microglia and astrocytes. Importantly, microglia and astrocytes expressed comparable levels of C3 mRNA which was similarly upregulated in both cell types in the App NL−G−F background. This finding supports a convergent activation of the complement pathway across both glial cell types, emphasizing its role as a central mediator of neuroinflammation. These observations align with growing evidence that both microglia and astrocytes serve as important producers of C3 in the CNS, although their relative contributions can vary by disease context. While microglia have long been considered the main professional phagocytes of the brain, astrocytes are increasingly recognized as active participants in neuroinflammation and even phagocytosis 28,60–63 . For instance, in the experimental autoimmune encephalomyelitis model of multiple sclerosis 13 , C3 expression was primarily attributed to microglia, whereas in a Parkinson’s disease (PD) model, astrocytic C3 was identified as a key contributor to pathology 16 . Another PD study reported that microglia dominated C3 expression at earlier stages, while astrocytes assumed the principal role later 14 . Furthermore, in a tri-culture model of AD with hPSC-derived microglia, astrocytes, and neurons, C3 production required both astrocytes and microglia, with astrocytic C3 secretion triggered by microglia, but also reciprocal microglial C3 production re-induced by astrocytes 64 . Lastly, studies showing that C3 is a hallmark of “A1” reactive astrocytes 65 , a subtype implicated in various neurodegenerative diseases, reinforce the notion that glial C3 expression can be highly context dependent. Collectively, these findings highlight the complexity of glial complement activation and underscore its potentially critical role in AD pathogenesis. Additionally, while our data reaffirm the established role of microglia as the main source of C1q in the CNS, we also observed upregulation of C1q in astrocytes in the App NL−G−F model. This finding adds to evidence suggesting that astrocytic C1q expression emerges under specific pathological conditions, as illustrated by several studies. For example, Orre et al ., using a similar transcriptional approach on isolated microglia and astrocytes from APPswe/PS1dE9 mice, found that both astrocytes and microglia adopted a proinflammatory phenotype, but the immune alterations in astrocytes were relatively more pronounced 66 . Interestingly, C1qa , C1qb , and C1qc were among the top DEGs in astrocytes but not in microglia, aligning with our dataset. Similarly, Iram et al . demonstrated that aged astrocytes in the 5xFAD model displayed increased C1q expression 67 . Their findings also suggested a functional role for astrocytic C1q in facilitating Aβ uptake. In human temporal lobe epilepsy, Aronica et al . reported C1q expression in astrocytes, further supporting the idea that astrocytes can upregulate C1q under pathological stress 68 . Additionally, Ingram et al . observed C1q immunolabeling in both reactive astrocytes and microglia in human multiple sclerosis tissue, particularly in plaque and peri-plaque regions 69 . Collectively, these studies demonstrate that while microglia remain the primary C1q source in the CNS, astrocytic C1q expression is context-dependent, emerging under specific pathological conditions. Although C3 is the central focus of this study, astrocytic C1q may add another layer of complement regulation, influencing glial crosstalk and complement-driven pathology. Although our transcriptomic findings suggest that microglia and astrocytes both upregulate C3 in aged App NL−G−F mice, the functional impact of this expression remained unclear. To address this, we generated novel microglia- (C3 mKO ) and astrocyte-specific (C3 aKO ) knockout models. Before disentangling the roles of microglia- and astrocyte-derived C3 in amyloid pathology, we first sought to explore their relative contributions in response to an acute neuroinflammatory stimulus, mimicked by ICV LPS injection. This allowed us to investigate glial-specific roles under inflammatory conditions, serving as a mechanistic proof-of-concept prior to extending our findings to the more complex AD-like pathology in App NL−G−F mice. This revealed that both microglia- and astrocyte-derived C3 are significant, as conditional C3 deficiency in either cell type led to a 50% reduction in the LPS-induced increase in C3 expression. The remaining C3 expression likely originates from the other glial cell type, pointing to an equal contribution. Moreover, we observed that the expression of TLR4-induced genes, such as Tnf and Il1β , peaked earlier at 4 hpi than complement upregulation at 3 dpi. However, in both C3 mKO and C3 aKO mice, the expression of these upstream proinflammatory genes was dampened, suggesting that complement might also crosstalk with TLR4 signaling to sustain or amplify neuroinflammation. This aligns with findings showing that complement activation amplifies TLR4-induced cytokine production in vivo , through C3a and C5a receptor signaling enhancing MAPK and NF-κB activation 70 . This raises the possibility that complement functions in a feedback loop to modulate the TLR4 response. Alternatively, the dampened LPS response in C3 mKO and C3 aKO mice could indicate that microglia, as the brain’s primary sensors of LPS, exhibit reduced sensitivity to TLR4 activation in the absence of glial C3. These findings underscore the bidirectional interplay between complement and inflammatory signaling 71,72 , which are likely relevant in chronic neurodegenerative diseases such as AD. Examining the downstream pathways activated by C3 in each glial cell type, could further delineate the functional roles of microglial and astrocytic C3. For example, microglial C3 may act through autocrine or paracrine signaling to modulate phagocytosis or cytokine release, whereas astrocytic C3 could engage distinct pathways to influence microglial function. It could be of interest to cross the C3 mKO and C3 aKO mice to generate a complete glial C3 knockout mouse. This approach would allow to determine whether total C3 expression in the CNS is further reduced, as either glial cell type may compensate for the loss of the other. It will also provide critical insights into whether this combined knockout has a more pronounced effect on the neuroinflammatory response, helping to clarify the relative and overlapping contributions of microglia- and astrocyte-derived C3. For microglial C3 deletion, we utilized Cx3Cr1 CreERT2 , a widely used and highly efficient line 73 . Although recombination after tamoxifen administration initially affects other myeloid subsets, these cells have short lifespans and are replaced by non-recombined progenitors within weeks, ensuring stable microglial C3 deficiency 74 . We confirmed the absence of recombination in peripheral myeloid cells via deflox PCR on BMDM-derived cultures. Nonetheless, brain-border macrophages at the choroid plexus, meninges, or perivascular spaces could still be targeted by this strategy 73 . To delete C3 in astrocytes, we employed Gfap Cre , which achieves broad astrocyte coverage 75,76 but can also drive recombination in some neural progenitor cells and neurons 77 . While we observed no non-astrocytic C3 deletion, minor off-target effects cannot be ruled out. More specific lines exist for each glial population: Tmem119 CreERT2 or P2ry12 CreERT2 may improve microglial specificity, albeit with lower recombination efficiency 73 , whereas Aldh1l1 CreERT2 can offer greater astrocyte specificity 78 but risks confounding expression in peripheral tissues 76,79 . Furthermore, an inducible CreERT2 approach specifically for astrocytes could provide finer temporal control, thereby avoiding potential developmental effects of C3 deficiency on the CNS and enabling a more precise understanding of how C3 mediates neuroinflammation and amyloid pathology in distinct glial populations. Despite the C3 expression by both microglia and astrocytes, neither C3 mKO nor C3 aKO impacted hippocampal C3 protein levels or amyloidosis in App NL−G−F mice in ageing or following mild peripheral inflammatory challenge by LPS. The latter model was employed to mimic inflammatory exposure more relevant to human conditions, which is absent in mice housed under SPF environments 31 . Importantly, LPS administration induced an expected transient drop in body temperature and body weight, which was unaffected by either the App NL−G−F background or the C3 genotype, ensuring a comparable inflammatory response across genotypes and providing a consistent baseline for evaluating the impact of C3 deletion in this AD model. One possibility is that non-targeted sources of C3, whether from the other glial cell type, peripheral C3, or non-glial cells, maintain overall hippocampal C3 levels. Interestingly, we also did not observe differences in hippocampal C3 protein levels between App NL−G−F and WT mice when focusing only on the C3 WT mice across all four experimental cohorts (C3 WT mice from both C3 mKO and C3 aKO lines, under both 40-week ageing and LPS-challenge paradigms), contrasting with a previous study reporting elevated C3b/iC3b levels in the brain of 9 month old App NL−G−F compared to WT mice 80 . The absence of detectable C3 signal in our C3 iKO mice confirms the specificity of our assay, suggesting that factors such as differences in age, experimental conditions, or the C3 fragment epitope recognized by the ELISA may account for this discrepancy between studies. Regardless, these findings highlight a disconnect between transcriptomic and proteomic levels, as we did detect a significant increase in C3 mRNA in microglia and astrocytes of App NL−G−F mice compared to WT controls. Despite this upregulation of C3 mRNA, the lack of any effect of C3 mKO or C3 aKO on Aβ plaque burden suggests that glial-derived C3 does not play a limiting role in amyloidosis under the tested conditions. Importantly, also C3 iKO mice, which effectively showed absence of C3 in the brain following tamoxifen administration, showed no altered Aβ plaque burden, soluble and insoluble Aβ 1−40 or A β1−42 levels, and glial activation. These results collectively indicate that C3 does not influence amyloid deposition or associated glial activation in the App NL−G−F model of AD when its removal is established at the age of 8 weeks. These findings contrast with previous studies using non-inducible full body C3 deficient APP/PS1 mice, which reported an increase in cerebral Aβ plaques but reduction in glial activation 24 . One key distinction from previous studies lies in the inducible nature of the C3 knockout used here. By deleting C3 in adult mice, we circumvent its potential roles during CNS development, including synaptic pruning and early immune regulation that might otherwise influence plaque initiation or aggregation events 11,12,25,81 . In contrast, constitutive germline knockouts, which lack C3 from birth, may drive more pronounced changes in amyloid burden or glial responses by disrupting these early developmental processes. Indeed, complement components such as C3 are known to shape neuronal circuits, and removing them during critical developmental windows could have long-lasting effects on plaque pathogenesis and inflammation in AD. A second factor that may contribute to our lack of observed effects is the App NL−G−F model itself, which expresses humanized APP under endogenous regulatory elements, reducing artifacts associated with overexpression 32 . By contrast, APP/PS1 mice overexpress both mutant APP and presenilin 1 (PS1), potentially amplifying the impact of C3 deficiency. Moreover, the Arctic mutation (E22G) in App NL−G−F mice enhances Aβ fibrillogenesis 82 and increases resistance to proteolytic degradation 83–85 , potentially affecting how amyloid plaques interact with inflammatory pathways. A similar case has been described for the inflammasome component NLRP3, where knockout effects were observed in APP/PS1 mice 86 but not in the App NL−G−F model 87 . Further studies are warranted to determine whether the limited impact of C3 deficiency in this study reflects the inducible timing of C3 removal, the App NL−G−F model background, or a combination of both. Crossbreeding constitutive C3 knockout lines with App NL−G−F or employing inducible knockouts in overexpression models such as APP/PS1 could help clarify these mechanisms and distinguish between developmental versus adult-specific roles of C3 in AD pathology. Several considerations should be noted when interpreting these findings. While the App NL−G−F model offers a refined approach to studying amyloidosis, its lack of tau pathology highlights the need for future studies incorporating models that also capture this AD hallmark. Furthermore, glial heterogeneity, both spatial and functional, was not fully explored in this study, and emerging techniques like single-cell RNA sequencing and spatial transcriptomics could provide deeper insights into region-specific and subset-specific glial responses. In addition, we did not investigate synaptic integrity, as this is addressed by Singh et al. (Lemere lab; unpublished results) in a complementary study, allowing us to focus on the neuroinflammatory aspects in this work. Importantly, our results on Aβ plaque load are in line with their parallel study. Lastly, while murine complement regulators differ from their human counterparts 88 , humanized chimeric models offer exciting opportunities to bridge this gap in future studies 89 . Despite these considerations, our study provides important insights into the cell-specific roles of C3 in AD. By employing conditional C3 knockout models, we demonstrated that glial C3 is a key player in neuroinflammation but does not significantly impact amyloid deposition in the App NL−G−F background. These results refine our understanding of complement’s role in AD, underscoring the complexity of C3-mediated pathways and the importance of further exploring targeted interventions. CONCLUSION This study demonstrates that both microglial and astrocytic C3 contribute to neuroinflammation but are insufficient to drive changes in amyloidosis in App NL−G−F mice. The lack of effects on Aβ load and glial activation observed in inducible full-body C3 knockout mice further challenges the previously proposed roles of complement in AD pathology. Together, our findings underscore the complexity of complement signaling in neuroinflammation and AD, highlighting the need for further investigation to unravel its multifaceted roles. Conditional C3 knockout models provide a valuable platform for exploring these mechanisms and identifying novel therapeutic strategies for AD. Abbreviations AD Alzheimer's disease ARIA Amyloid-related imaging abnormalities Aβ Amyloid beta BMDM Bone marrow-derived macrophages C3 aKO C3 astrocyte-specific knockout C3 iKO C3 inducible full-body knockout C3 mKO C3 microglia-specific knockout cpm Counts per million CSF Cerebrospinal fluid DAM Disease-associated microglia DEGs Differentially expressed genes dpi Days post injection EUCOMM European Conditional Mouse Mutagenesis Program FACS Fluorescence-activated cell sorting GO Gene ontology hpi Hours post injection ICV Intracerebroventricular KO Knockout LPS Lipopolysaccharide PBS Phosphate-buffered saline PD Parkinson’s disease PS1 Presenilin 1 RNA-seq RNA sequencing RT Room temperature SPF Specific pathogen-free TPM Transcripts per million Declarations Ethics approval and consent to participate All mouse experiments complied with the current laws of Belgium (Law of 14. August 1986 related to protection and welfare of animals) and EU directive 2010/63/EU, and were approved by the animal ethics committee of Ghent University (EC 2021-003, 2023-001, 2024-035, 2024-036). Consent for publication Not applicable. Availability of data and material The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the Research Foundation-Flanders (FWO Vlaanderen; 3F003118; 3F013720; 1268823N; 1295223N) and the Alzheimer Research Foundation (SAO-FRA; 20190028). Authors' contributions PD, CV and REV designed the research; PD, JF, RV, MB, CDN, EVW, GVI, LVH and CV performed and/or assisted with experiments. TH designed and generated the transgenic mice. PD, JF, RV, CDN, LVH, CV and REV analyzed and/or interpreted the data. PD, CV and REV wrote the manuscript. 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A novel mouse model expressing human forms for complement receptors CR1 and CR2. BMC Genet. 21 , 101 (2020). Mancuso, R. et al. Xenografted human microglia display diverse transcriptomic states in response to Alzheimer’s disease-related amyloid-β pathology. Nat. Neurosci. 27 , 886–900 (2024). Tables Table 1 . Sequences of the forward and reverse primers used for deflox PCR, genotyping PCR and RT-qPCR. Genotyping and deflox PCR Gene Forward (5'-3') Reverse1 (5'-3') Reverse2 (5'-3') C3 CTTAACTCAAACTCCCAGCAC CTCCAGTACGATGGTCTCTT GTTCAAATCCCTACTGTGCC Deflox RT-qPCR C3 tm1d AAGTATAGGAACTTCGTCGAGATA GTGGACTGAGTTACCAGTAATTTG C3 exon 5 ATCATAGCCAAGTGAGGATGG GTACAGGAACCTGAGAGACAAG RT-qPCR Rpl CCTGCTGCTCTCAAGGTT TGGTTGTCACTGCCTCGTACTT Hprt AGTGTTGGATACAGGCCAGAC CGTGATTCAAATCCCTGAAGT Ubc AGGTCAAACAGGAAGACAGACGTA TCACACCCAAGAACAAGCACA Gapdh TGAAGCAGGCATCTGAGGG CGAAGGTGGAAGAGTGGGAG Tnf ACCCTGGTATGAGCCCATATAC ACACCCATTCCCTTCACAGAG Il1β CACCTCACAAGCAGAGCACAAG GCATTAGAAACAGTCCAGCCCATAC Il6 TAGTCCTTCCTACCCCAATTTCC TTGGTCCTTAGCCACTCCTTC Inos CCTCAGGGGTTATTGGACTGG GGGGACACACACTATCTCTCT C3 CCAGCTCCCCATTAGCTCTG GCACTTGCCTCTTTAGGAAGTC C1qa AAAGGCAATCCAGGCAATATCA TGGTTCTGGTATGGACTCTCC C1r GCCATGCCCAGGTGCAAGATCAA TGGCTGGCTGCCCTCTGATG C1s TGGACAGTGGAGCAACTCCGGT GGTGGGTACTCCACAGGCTGGAA Aif1 ATCAACAAGCAATTCCTCGATGA CAGCATTCGCTTCAAGGACATA Cd11b ATGGACGCTGATGGCAATACC TCCCCATTCACGTCTCCCA Lcn2 CCAGTTCGCCATGGTATTTT CACACTCACCACCCATTCAG Serpina3a ATTTGTCCCAATGTCTGCGAA TGGCTATCTTGGCTATAAAGGGG Table 2. Antibodies used for flow cytometry and immunohistochemistry Antibody Label Dilution Company Reference number Flow cytometry Fixable Viability Dye eFluor 506 1/1000 eBioscience 65-0866-14 CD45 BUV805 1/400 BD Biosciences 748370 CD11b BUV395 1/400 BD Biosciences 563553 ACSA2 PE-Cy7 1/140 Miltenyi Biotec 130-123-284 O1 eFluor660 1/810 eBioscience 50-6506-82 Ly6C eFluor450 1/200 BD Biosciences 553104 Immunohistochemistry 6E10 1/500 Biolegend 803001 GFAP 1/1000 Dako Z033429-2 IBA1 1/10000 Wako Chemicals 019-19741 Anti-rabbit Biotin 1/1000 Thermo Scientific 65-6140 Additional Declarations No competing interests reported. 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20:38:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6597252/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6597252/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83618601,"identity":"7f1a1c7d-d119-4e3e-bc9f-2b9a23c09c78","added_by":"auto","created_at":"2025-05-29 14:26:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":150538,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComplement is upregulated in microglia and astrocytes from 40 weeks old \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eApp\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eNL-G-F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e mice compared to age-matched wild-type (WT) C57BL/6J mice. (a) \u003c/strong\u003eExperimental set-up and heatmap visualizing log\u003csub\u003e2\u003c/sub\u003e(TPM) expression levels of cell type specific markers of microglia (\u003cem\u003eAif1\u003c/em\u003e,\u003cem\u003e Tmem119\u003c/em\u003e,\u003cem\u003e Itgam\u003c/em\u003e,\u003cem\u003e P2ry12\u003c/em\u003e), astrocytes (\u003cem\u003eSox9\u003c/em\u003e, \u003cem\u003eAldh1l1\u003c/em\u003e, \u003cem\u003eS100b\u003c/em\u003e, \u003cem\u003eGfap\u003c/em\u003e), neurons (\u003cem\u003eTubb3\u003c/em\u003e, \u003cem\u003eDcx\u003c/em\u003e, \u003cem\u003eNefh\u003c/em\u003e), oligodendrocytes (\u003cem\u003eMog)\u003c/em\u003e, oligodendrocytes precursor cells (\u003cem\u003eSox10\u003c/em\u003e), pericytes (\u003cem\u003eSlc6a12\u003c/em\u003e) and endothelial cells (\u003cem\u003eMcam\u003c/em\u003e) in isolated microglia and astrocytes from age-matched WT (n=4) and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e (n=5) mice. \u003cstrong\u003e(b,c)\u003c/strong\u003e Top 10 enriched gene ontology biological pathways in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e microglia (b) and astrocytes (c). Biological pathways are ordered according to the ratio of the input differentially expressed gene DEG set annotated in the respective gene ontology (GO) terms (geneRatio). \u003cstrong\u003e(d,e) \u003c/strong\u003eVolcano plot showing microglial (d) and astrocytic (e)\u003cstrong\u003e \u003c/strong\u003egene expression changes in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e mice.\u003cstrong\u003e \u003c/strong\u003eDotted lines indicate adjusted p-value \u0026gt; 0.01 and |log\u003csub\u003e2\u003c/sub\u003eFC| \u0026gt; 1cut-offs for differential expression. Selected complement genes are manually labeled. \u003cstrong\u003e(f,g) \u003c/strong\u003eHeatmap showing gene expression changes in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e microglia (f) and astrocytes (g) of the top25 DEGs (ranked on |log\u003csub\u003e2\u003c/sub\u003eFC|) and complement genes (ranked in order of the complement cascade).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/368a73ec431f6c3fc8c3657f.jpg"},{"id":83617849,"identity":"19cf3d11-8a24-4a11-b924-3c906e2c7255","added_by":"auto","created_at":"2025-05-29 14:18:10","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":152230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglial and astrocytic C3 are relevant neuroinflammatory factors. (a) \u003c/strong\u003eqRT-PCR analysis of proinflammatory genes \u003cem\u003eTnf\u003c/em\u003e, \u003cem\u003eIl1β\u003c/em\u003e, \u003cem\u003eIl6\u003c/em\u003e, \u003cem\u003eInos\u003c/em\u003e, and complement genes \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1r\u003c/em\u003e, \u003cem\u003eC1s\u003c/em\u003e in the cortex 4 h and 3 d after the intracerebroventricular (ICV) injection of PBS \u003cem\u003eversus\u003c/em\u003e LPS. \u003cstrong\u003e(b, c)\u003c/strong\u003e qRT-PCR analysis of proinflammatory genes \u003cem\u003eTnf\u003c/em\u003e, \u003cem\u003eIl1β\u003c/em\u003e, \u003cem\u003eIl6\u003c/em\u003e, \u003cem\u003eInos\u003c/em\u003e, complement genes \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1r\u003c/em\u003e, \u003cem\u003eC1s\u003c/em\u003e, microglial activation marker genes \u003cem\u003eAif1\u003c/em\u003e, \u003cem\u003eCd11b\u003c/em\u003e, and astrocytic activation marker genes \u003cem\u003eLcn2\u003c/em\u003e, \u003cem\u003eSerpina3a\u003c/em\u003e in the cortex 3 d after the ICV injection of PBS \u003cem\u003eversus\u003c/em\u003e LPS in C3\u003csup\u003emKO\u003c/sup\u003e (C3\u003csup\u003efl/fl\u003c/sup\u003e; Cx3Cr1\u003csup\u003eCreERT2/+\u003c/sup\u003e) (b) or C3\u003csup\u003eaKO\u003c/sup\u003e (C3\u003csup\u003efl/fl\u003c/sup\u003e; Gfap\u003csup\u003eCre\u003c/sup\u003e) (c) \u003cem\u003eversus\u003c/em\u003e C3\u003csup\u003eWT\u003c/sup\u003e littermates. Statistics: the mean ± SD are shown with two-way ANOVA with post-hoc Tukey’s test. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/e93c5963b33f4c5fb91d2690.jpg"},{"id":83617856,"identity":"f240b4cd-d62f-4b32-af16-d392a18ef62f","added_by":"auto","created_at":"2025-05-29 14:18:10","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":176702,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicroglial and astrocytic C3 deficiency does not affect C3 protein levels and amyloidosis in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eApp\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eNL-G-F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e model. (a, f) \u003c/strong\u003eExperimental setup for \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003emKO\u003c/sup\u003e (a) and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e\u003cem\u003e; \u003c/em\u003eC3\u003csup\u003eaKO \u003c/sup\u003emice (f). Mice were aged until 40 weeks or subjected to mild peripheral inflammation by two IP injections of 1.0 mg/kg LPS at 22 and 23 weeks following analysis at 25 weeks. C3\u003csup\u003emKO\u003c/sup\u003e mice were subcutaneously injected with tamoxifen (20 mg/mL) twice, 2 d apart, at the age of 4 weeks \u003cstrong\u003e(b, d, g, i)\u003c/strong\u003e Hippocampal C3 levels measured by C3 ELISA in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003emKO\u003c/sup\u003e (b, d) or \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eaKO\u003c/sup\u003e (g,i) \u003cem\u003eversus\u003c/em\u003e \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates and \u003cem\u003eversus\u003c/em\u003e aged-matched non-AD C57BL/6J controls in response to ageing (b, g) and mild peripheral inflammation (d, i). \u003cstrong\u003e(c, e, h, j) \u003c/strong\u003eQuantification of 6E10\u003csup\u003e+\u003c/sup\u003e Aβ plaques in the hippocampus of \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003emKO\u003c/sup\u003e (c, e) or \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eaKO\u003c/sup\u003e (h, j) \u003cem\u003eversus\u003c/em\u003e \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates in response to ageing (c, h) and mild peripheral inflammation (e, j). Images representative for the biological replicates, scale bar: 250 µm. Statistics: the mean ± SD are shown with two-way ANOVA with post-hoc Tukey’s multiple comparison test.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/5ddf600d71f299c53e5392be.jpg"},{"id":83617850,"identity":"1116ec95-14a6-4c4d-9451-8a03624fa823","added_by":"auto","created_at":"2025-05-29 14:18:10","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":160143,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInducible full-body C3 deficiency does not affect amyloidosis in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eApp\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eNL-G-F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e model. (a, f) \u003c/strong\u003eExperimental setup for the analysis of \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e mice.\u003cstrong\u003e \u003c/strong\u003eMice were analyzed upon either ageing (a) or mild peripheral inflammation (f) in which mice were sampled at the age of 40 weeks or subjected to two IP injections of 1.0 mg/kg LPS at 22 and 23 weeks and sampled at 25 weeks, respectively. The KO was induced at the age of 8 weeks via tamoxifen (50 mg/mL) oral gavage, daily, for 5 days. \u003cstrong\u003e(b, g)\u003c/strong\u003e Hippocampal C3 levels measured by C3 ELISA in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eaKO\u003c/sup\u003e \u003cem\u003eversus\u003c/em\u003e \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates in response to\u003cstrong\u003e \u003c/strong\u003eageing (b) and mild peripheral inflammation (g). Statistics: the mean ± SD are shown with 2-tailed Student’s t-test. \u003cstrong\u003e(c, h) \u003c/strong\u003eQuantification of 6E10\u003csup\u003e+\u003c/sup\u003e Aβ plaques in the hippocampus of \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e \u003cem\u003eversus\u003c/em\u003e \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates in response to ageing (c) and mild peripheral inflammation (h). Images representative for the biological replicates, scale bar: 250 µm. Statistics: the mean ± SD are shown with two-way ANOVA with post-hoc Tukey’s multiple comparison test. \u003cstrong\u003e(d, e, i, j) \u003c/strong\u003eAβ\u003csub\u003e1-40\u003c/sub\u003e\u0026nbsp;and Aβ\u003csub\u003e1-42\u003c/sub\u003e\u0026nbsp;levels in soluble (d, i) and insoluble (e, j) hippocampal protein extractions in response to ageing (d, e) and mild peripheral inflammation (i, j). Statistics: the mean ± SD are shown with 2-tailed Student’s t-test. ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/cc7a4f1fff34d148d9187f6c.jpg"},{"id":83618602,"identity":"c10acba3-c810-4335-93e4-c567ea4cf01a","added_by":"auto","created_at":"2025-05-29 14:26:10","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":187695,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInducible full-body C3 deficiency does not affect neuroinflammation in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eApp\u003c/strong\u003e\u003c/em\u003e\u003csup\u003e\u003cstrong\u003eNL-G-F\u003c/strong\u003e\u003c/sup\u003e\u003cstrong\u003e model. (a-f) \u003c/strong\u003eMicroglial analyses in 40 weeks old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates. \u003cstrong\u003e(a) \u003c/strong\u003eRepresentative images of IBA1 staining in the hippocampus. Scale bars: 250 µm in overview, 50 µm in zoom-ins. \u003cstrong\u003e(b-d) \u003c/strong\u003eImage quantification of IBA1\u003csup\u003e+\u003c/sup\u003e cell number (b), length (c), and circularity (d). \u003cstrong\u003e(e-f)\u003c/strong\u003e qRT-PCR analysis of microglial activation marker genes \u003cem\u003eAif1\u003c/em\u003e (e) and\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eCd11b\u003c/em\u003e (f) on cortex. \u003cstrong\u003e(g-l) \u003c/strong\u003eAstrocytic analyses in the same 40 weeks old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL-G-F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates. \u003cstrong\u003e(g) \u003c/strong\u003eRepresentative images of GFAP staining in the hippocampus. Scale bars: 250 µm in overview, 50 µm in zoom-ins. \u003cstrong\u003e(h)\u003c/strong\u003e Image quantification of GFAP\u003csup\u003e+\u003c/sup\u003e area. \u003cstrong\u003e(i-l)\u003c/strong\u003e qRT-PCR analysis of astrocytic activation marker genes \u003cem\u003eGfap \u003c/em\u003e(i), \u003cem\u003eAldh1l1 \u003c/em\u003e(j), \u003cem\u003eLcn2\u003c/em\u003e (k) and\u003cstrong\u003e \u003c/strong\u003e\u003cem\u003eSerpina3a\u003c/em\u003e (l) on cortex. Statistics: the mean ± SD are shown with 2-tailed Student’s t-test.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/774a835a7bd7225b6c3845e2.jpg"},{"id":83618754,"identity":"32fb49f6-264b-47d1-8503-80a0049c5fa3","added_by":"auto","created_at":"2025-05-29 14:34:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2816679,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/6388a741-6ebb-4fa1-bc95-0f8361da0e58.pdf"},{"id":83617855,"identity":"3200a5f7-fecf-4ac7-b477-e1457013c1fe","added_by":"auto","created_at":"2025-05-29 14:18:10","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12173526,"visible":true,"origin":"","legend":"","description":"","filename":"SUPPLEMENTARYINFORMATION.docx","url":"https://assets-eu.researchsquare.com/files/rs-6597252/v1/a07e17863deb21b46452286b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Glial-specific and inducible full body C3 deficiency does not affect amyloid pathology in the AppNL-G-F mouse model of Alzheimer’s disease","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eAlzheimer\u0026rsquo;s disease (AD) is a progressive neurodegenerative disorder characterized by the accumulation of amyloid-beta (Aβ) plaques and tau neurofibrillary tangles, accompanied by widespread neuroinflammation, synaptic dysfunction, and neuronal loss \u003csup\u003e1\u003c/sup\u003e. As the leading cause of dementia worldwide, AD remains a significant public health challenge, with its prevalence expected to rise as populations age \u003csup\u003e2\u003c/sup\u003e. Although significant advancements in Aβ-targeting therapies have been made, including monoclonal antibodies such as lecanemab and aducanumab \u003csup\u003e3\u003c/sup\u003e, these approaches primarily target Aβ clearance, without addressing the underlying processes. Furthermore, these treatments have shown limited clinical efficacy with unfavorable risk/benefit profiles due to amyloid-related imaging abnormalities (ARIA) \u003csup\u003e4,5\u003c/sup\u003e. This underscores the growing need for fundamental research into upstream mechanisms of AD pathology, such as neuroinflammation \u003csup\u003e6,7\u003c/sup\u003e. Among the various immune pathways implicated, the complement system has emerged as a key mediator of neuro-inflammatory processes in AD, which may offer additional therapeutic targets \u003csup\u003e8,9\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe complement system is a tightly regulated cascade of over 30 soluble and membrane-bound proteins, traditionally associated with immune defense through its roles in opsonization, cell lysis, and recruitment of immune cells \u003csup\u003e10\u003c/sup\u003e. It can be activated through the classical, lectin and alternative pathways, that importantly all converge via the cleavage of the central component C3 into the biologically active fragments C3a and C3b. Beyond its classical functions in innate immunity, complement has essential roles in the central nervous system (CNS), most notably as a mediator of synaptic pruning during neuronal circuit refinement \u003csup\u003e11,12\u003c/sup\u003e. In the brain, \u003cem\u003eC3\u003c/em\u003e expression has been primarily attributed to glial cells, including microglia \u003csup\u003e13,14\u003c/sup\u003e and astrocytes \u003csup\u003e14\u0026ndash;16\u003c/sup\u003e, and its activity is tightly regulated to prevent excessive inflammation.\u003c/p\u003e \u003cp\u003eIn AD, dysregulation of the complement system is thought to contribute to pathology, as exemplified by increased C3 levels in cerebrospinal fluid (CSF) and postmortem brain of AD patients \u003csup\u003e15,17\u0026ndash;19\u003c/sup\u003e. Decades ago, it was discovered that Aβ can bind and activate complement \u003csup\u003e20\u0026ndash;22\u003c/sup\u003e, aiding its clearance. In addition, complement binding to Aβ has also been shown to promote Aβ aggregation \u003csup\u003e23\u003c/sup\u003e, and excessive complement activity exacerbates synaptic pruning, microgliosis, and astrocytosis, contributing to neurodegeneration \u003csup\u003e15,18,24\u0026ndash;28\u003c/sup\u003e. For example, C3 deficiency in aged plaque-rich APP/PS1 mice protects against synapse and neuron loss, decreases glial reactivity, and spares cognitive decline, despite an increased plaque burden \u003csup\u003e24\u003c/sup\u003e. However, these findings largely stem from studies using classical germline C3 knockout mice and APP-overexpressing AD models, which have significant limitations. Germline C3 knockouts fail to account for the temporal and cell-specific roles of C3, and first-generation AD transgenic mouse models artificially overexpress mutant APP potentially causing additional phenotypes that do not fully recapitulate the amyloid pathology observed in human AD \u003csup\u003e29\u003c/sup\u003e. Additionally, discrepancies between studies regarding C3\u0026rsquo;s effects have been attributed to the use of different Aβ-overexpression models, further complicating interpretations. These limitations highlight the need for more precise tools to dissect the role of C3 in AD. Finally, the mice used in these studies are typically housed under specific pathogen-free (SPF) conditions, which fail to replicate the inflammatory environment experienced by humans and may overlook critical interactions between systemic inflammation and disease progression \u003csup\u003e30,31\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eTo address these limitations, this study employs the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e knock-in mouse model \u003csup\u003e32\u003c/sup\u003e of AD combined with novel microglia-specific (C3\u003csup\u003emKO\u003c/sup\u003e), astrocyte-specific (C3\u003csup\u003eaKO\u003c/sup\u003e), and inducible full-body (C3\u003csup\u003eiKO\u003c/sup\u003e) C3 knockout mice, enabling glial specific and temporally controlled deletion of C3. Our study was performed in parallel with a similar study by Singh et al. (Lemere lab; unpublished results) in an accompanying paper. Moreover, we incorporated a previously introduced AD mouse model \u003csup\u003e30,31\u003c/sup\u003e in which we administer lipopolysaccharide (LPS) to 5 months old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice to mimic multiple peripheral inflammatory episodes. Using these models, we investigated the impact of C3 deficiency in response to neuroinflammation as well as during AD pathology. As such, this study provides new insights into the nuanced roles of C3 in neuroinflammation, and highlights the importance of further research to untangle the dualistic nature of complement-mediated events in AD.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003eMice were housed with 14- to 10-h light and dark cycles and received \u003cem\u003ead libitum\u003c/em\u003e food and water in individually ventilated cages under specific pathogen-free (SPF) conditions. Conditional C3 KO mice (\u003cem\u003eC3\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e; C57BL/6J background) were generated in house, as described below. The generation of \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice carrying Arctic, Swedish, and Beyreuther/Iberian mutations was described previously \u003csup\u003e32\u003c/sup\u003e. In all experiments age- and gender- matched littermates were used. All experiments complied with the current laws of Belgium (Law of 14. August 1986 related to protection and welfare of animals) and EU directive 2010/63/EU, and were approved by the animal ethics committee of Ghent University (EC 2021-003, 2023-001, 2024-035, 2024-036).\u003c/p\u003e \u003cp\u003e \u003cb\u003eGeneration of\u003c/b\u003e \u003cb\u003eC3\u003c/b\u003e\u003csup\u003e\u003cb\u003efl/fl\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emice\u003c/b\u003e\u003c/p\u003e \u003cp\u003eConditionally targeted embryonic stem (ES) cells (JM8A3.N1) containing the C3 \u003cem\u003etm1a\u003c/em\u003e knockout first allele with floxed exons 2\u0026ndash;4 were obtained from the European Mouse Mutant Cell Repository (EUMMCR) \u003csup\u003e33\u003c/sup\u003e. ES cells were injected into C56BL/6J blastocysts which were transferred to pseudopregnant B6CBAF1 foster mothers. Resulting coat color chimeras were crossed with C56BL/6J females to check for germline transmission. Germline offspring containing the tm1a allele were crossed with Flpe deleter mice \u003csup\u003e34\u003c/sup\u003e to remove the LacZ reporter and the Neomycin selection marker to obtain the conditional \u003cem\u003etm1c\u003c/em\u003e allele. Genotyping of the \u003cem\u003eC3\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e mice was performed on crude DNA extracts from toe tissue samples. Primers used to genotype the mice are provided in \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e. \u003cem\u003eC3\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e mice were crossed with \u003cem\u003eCx3Cr1\u003c/em\u003e\u003csup\u003eCreERT2/+\u003c/sup\u003e (B6.129(C)-Cx3cr1\u003csup\u003etm2.1(cre/ERT2)Jung\u003c/sup\u003e/Orl) \u003csup\u003e35\u003c/sup\u003e, \u003cem\u003eGfap\u003c/em\u003e\u003csup\u003eCre/+\u003c/sup\u003e (Tg(GFAP-cre)8Gtm) \u003csup\u003e36\u003c/sup\u003e or \u003cem\u003eRosa26\u003c/em\u003e\u003csup\u003eCreERT2\u003c/sup\u003e (B6.129-\u003cem\u003eGt(ROSA)26Sor\u003c/em\u003e\u003csup\u003e\u003cem\u003etm1(cre/ERT2)Tyj\u003c/em\u003e\u003c/sup\u003e/J) mice to generate tamoxifen inducible microglia-specific (C3\u003csup\u003emKO\u003c/sup\u003e), astrocyte-specific (C3\u003csup\u003eaKO\u003c/sup\u003e), and tamoxifen inducible full-body (C3\u003csup\u003eiKO\u003c/sup\u003e) C3 knockout mice, respectively.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eTamoxifen administration\u003c/h3\u003e\n\u003cp\u003eC3\u003csup\u003emKO\u003c/sup\u003e mice were subcutaneously injected with 20 mg/mL tamoxifen (Sigma-Aldrich; T5648) dissolved in corn oil (Sigma-Aldrich; C8267) twice, 2 days apart, at the age of 4 weeks, as described before \u003csup\u003e37,38\u003c/sup\u003e. For C3\u003csup\u003eiKO\u003c/sup\u003e mice, animals were administered tamoxifen (50 mg/mL) dissolved in corn oil with 10% ethanol, via oral gavage, daily, for 5 days, at the age of 8 weeks, as determined via optimization to reach the highest possible induced knockout efficiency. Also C3\u003csup\u003eWT\u003c/sup\u003e littermates received the same tamoxifen protocol to control for potential off-target effects.\u003c/p\u003e\n\u003ch3\u003eIntracerebroventricular injection\u003c/h3\u003e\n\u003cp\u003eICV injections were performed as described before \u003csup\u003e39\u003c/sup\u003e. Mice were anesthetized with isoflurane and mounted on a stereotactic frame. A constant body temperature of 37\u0026deg;C was maintained using a heating pad. Injection coordinates were measured relative to the bregma intersection (anteroposterior \u0026minus;\u0026thinsp;0.7 mm, mediolateral\u0026thinsp;+\u0026thinsp;1.0 mm, dorsoventral \u0026minus;\u0026thinsp;2.0 mm) and were determined using the Franklin and Paxinos mouse brain atlas. By using a Hamilton needle, 5 \u0026micro;L of either PBS or LPS from \u003cem\u003eSalmonella enterica\u003c/em\u003e serotype abortus equi (Sigma-Aldrich; L-5886) (1 \u0026micro;g/mL) was injected into the left lateral ventricle. The subsequent sampling was performed 3 days after the ICV injections.\u003c/p\u003e\n\u003ch3\u003eInduction of mild peripheral inflammation\u003c/h3\u003e\n\u003cp\u003eLPS from \u003cem\u003eSalmonella enterica\u003c/em\u003e serotype abortus equi (Sigma-Aldrich; L-5886) intraperitoneal (IP) injections (1.0 mg/kg body weight) were performed on day 0 and 7 at the age of 22 weeks, as previously described \u003csup\u003e30,31\u003c/sup\u003e. Mice were sacrificed two weeks after the second LPS injection at the age of 25 weeks. Body weight, temperature loss, and sickness behavior were checked daily for a week after each LPS injection.\u003c/p\u003e\n\u003ch3\u003eTissue sample collection\u003c/h3\u003e\n\u003cp\u003eMice were sedated through IP injection with an overdose of ketamine (87.5 mg/kg) and xylazine (12.5 mg/kg). After disappearance of paw and tail reflexes, mice were transcardially perfused using 10 mL 0.2% heparin (Sigma; H-3125) in ice-cold D‐phosphate-buffered saline (PBS) (Gibco; 14190‐094) per mouse (4.50 mL/min). For preparation of single cell suspensions, brains were carefully isolated from the skull and collected in 1.5 mL of ice‐cold 1\u0026times; Hanks\u0026rsquo; balanced salt solution (HBSS)-/- (Gibco; 14175/053). Samples were kept on ice and immediately processed. For all other analyses (immunohistochemistry, RNA/protein analysis), brains were carefully extracted from the skull and split into two hemispheres (mid-sagittal). From the left hemisphere, the hippocampus and cortex were micro-dissected, snap-frozen in liquid nitrogen and stored at -80\u0026deg;C until further use. The right hemisphere was fixed immediately in 4% PFA overnight (ON) for approximately 16 h at 4\u0026deg;C. Next, the right hemisphere was again split in half (mid-coronal). The right posterior half hemisphere was dehydrated and embedded in paraffin and stored at room temperature (RT) until further use. The right anterior half hemisphere was embedded in 5% 2-hydroxyethylagarose and stored at 4\u0026deg;C until further use.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFACS of microglia and astrocytes from whole mouse brain\u003c/h2\u003e \u003cp\u003eFor concurrent microglia and astrocyte isolation, the protocol was adapted from previous reports \u003csup\u003e40,41\u003c/sup\u003e. Brain samples were collected in ice-cold 1\u0026times; HBSS-/-, and cut to pieces approximately 1 mm\u003csup\u003e3\u003c/sup\u003e in size using spring scissors. Brain slurry was dissociated into single cell suspensions using the Neural Tissue Dissociation Kit (P) (Miltenyi Biotec; 130-092‐628). Samples were always kept on ice unless stated otherwise. Cells were enzymatically dissociated using activated enzyme (P) for 15 min at 37\u0026deg;C and enzyme (A) for 2 \u0026times; 10 min at 37\u0026deg;C under continuous nutation. Additionally, the samples were mechanically dissociated by trituration in between enzymatic dissociation steps. To stop the enzymatic reaction, samples were diluted with an excess of 1\u0026times; HBSS-/-. The samples were then passed through a 70 \u0026micro;M cell strainer (BD Falcon; 734‐0003) and mixed with 90% Percoll\u0026trade; (Merck; GE17‐5445‐02) PLUS equilibrated in HBSS\u0026minus;/\u0026minus; pH 7.4 and to obtain a final concentration of 24% Percoll\u0026trade; PLUS. Next, the samples were spun down at 300g for 11 min at RT with a low acceleration and deceleration brake. The myelin layer and supernatant were aspirated and the pellet was resuspended in 50 \u0026micro;L of 0.5% bovine serum albumin (BSA) (Jackson ImmunoResearch; 001‐000‐162) in D‐PBS (Gibco; 14190‐094). Single cell suspensions were pre-incubated with Fc Block (1/100) (BD Biosciences; 553142) for 10 min at 4\u0026deg;C and stained with appropriate antibodies at 4\u0026deg;C in the dark for 30 min. Antibodies and dilutions are listed in \u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e. Reactions were stopped by adding an excess of staining buffer, cells were spun down at 400g for 7 min at 4\u0026deg;C. Pellets were resuspended in FACS buffer and transferred through a 35 \u0026micro;m mesh into a 5 mL Falco\u0026reg;Round‐Bottom Polystyrene Test Tube with Cell Strainer Snap Cap (Fisher Scientific; 08‐771‐23). Cell viability was assessed using DAPI 1/200, added immediately prior to sorting. Flow cytometry and cell sorting was performed on the FACSymphony S6 using the 85 \u0026micro;m nozzle. Cells were sorted into 2 mL Eppendorf tubes containing 450 \u0026micro;L RLT Plus lysis buffer (Qiagen) containing 1% β-mercaptoethanol. The samples were extensively vortexed and stored at -80\u0026deg;C until RNA isolation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA sequencing on isolated microglia and astrocytes\u003c/h3\u003e\n\u003cp\u003eRNA from sorted cells was isolated using the RNeasy Plus Micro Kit (Qiagen; 74034) according to the manufacturer\u0026rsquo;s instructions. The concentration and purity of the RNA was determined using the Nanodrop ND-1000 (Nanodrop Technologies, Thermo Scientific) and the Agilent 2100 Bio-Analyzer. After cDNA library preparation with the Illumina Stranded Total RNA prep (Illumina), sequencing was carried out on an Illumina NovaSeq 6000 instrument.\u003c/p\u003e \u003cp\u003ePreprocessing of the RNA-seq data was performed by Trimmomatic v0.39 \u003csup\u003e42\u003c/sup\u003e and quality control by FastQC v0.11.8 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.babraham.ac.uk/projects/fastqc/\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.babraham.ac.uk/projects/fastqc/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Mapping to the reference mouse genome was accomplished by STAR v2.7.3a, BAM files were created with Samtools v1.9 and HTSeqCount v0.11.2 was used for counting \u003csup\u003e43,44\u003c/sup\u003e. The data was split up by cell type (microglia and astrocytes) and analyzed separately. EdgeR v3.32.1 was used to normalize both datasets \u003csup\u003e45\u003c/sup\u003e. One sample from the C57BL/6J control group was identified as an outlier in both datasets. This sample was removed from downstream analysis. Genes which did not meet the requirement of a count per million (cpm) larger than 1 in at least the number of samples equaling the smallest group size, 4 for both astrocytes and microglia, were filtered out. This resulted in an expression table containing 14141 genes and 9 samples for the astrocyte dataset and an expression table containing 12902 genes and 9 samples for the microglia dataset. A Limma-voom pipeline v3.46.0 was utilized to perform differential expression (DE) analysis \u003csup\u003e46\u003c/sup\u003e. Benjamini-Hochberg correction was used to adjust the p-values for multiple testing. To be labeled as a DE gene, a gene needed to have an adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and a log\u003csub\u003e2\u003c/sub\u003eratio\u0026thinsp;\u0026gt;\u0026thinsp;0.5 or \u0026lt; -0.5. The R package pheatmap v1.0.12 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://CRAN.R-project.org/package=pheatmap\u003c/span\u003e\u003cspan address=\"https://CRAN.R-project.org/package=pheatmap\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to create a heatmap of the top 25 DE genes (according to adjusted p-value) between the WT C57BL/6J and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e group for the astrocyte and microglia dataset respectively. Additionally, a heatmap was created with a selection of expressed complement genes (containing DEGs and non-DEGs) for both celltypes. In all heatmaps the displayed gene expression was log\u003csub\u003e2\u003c/sub\u003e normalized. The mean expression value per gene over all samples (per dataset) was calculated and then subtracted from each sample's particular gene expression value to scale the expression values. The R package EnhancedVolcano v1.20.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/kevinblighe/EnhancedVolcano\u003c/span\u003e\u003cspan address=\"https://github.com/kevinblighe/EnhancedVolcano\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to create volcano plots to visualize the results of the DE analyses between the WT C57BL/6J and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e group for the astrocyte and microglia dataset respectively. The figures plot out the -log\u003csub\u003e10\u003c/sub\u003e adjusted p-value on the Y-axis versus the log\u003csub\u003e2\u003c/sub\u003eFC value on the X-axis for all genes in the respective expression table. Based on the utilized cut-offs (see above), genes were colored differently: red genes are significant DE genes, blue genes only meet the adjusted p-value cut-off, green genes only meet the log\u003csub\u003e2\u003c/sub\u003eFC cut-off and black genes don\u0026rsquo;t meet either requirement. A selection of complement genes was manually chosen and the expressed genes were labeled in the plot. To be able to compare the expression values of certain genes across datasets, a separate analysis was performed with astrocyte and microglia samples together. The same outliers were discarded as in the previous analysis. Instead of calculating log\u003csub\u003e2\u003c/sub\u003eCPM values and performing TMM (trimmed mean of M values) normalization as before, log\u003csub\u003e2\u003c/sub\u003eTPM values were calculated in this combined analysis. This takes into account the length of a gene and facilitates the comparison of expression values between different genes. Gene ontology (GO) enrichment analysis was performed using the clusterProfiler R package v4.10.0 \u003csup\u003e47\u003c/sup\u003e. This was conducted on the DE gene sets of the DE analyses between the WT C57BL/6J and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e group for the astrocyte and microglia dataset respectively. The full gene list of the respective expression tables was used as background for the enrichment analysis. All ontologies (\u0026ldquo;Biological Pathway\u0026rdquo;, \u0026ldquo;Molecular Function\u0026rdquo; and \u0026ldquo;Cellular Compartment\u0026rdquo;) were included and an adjusted p-value cut-off of 0.05 was utilized. The top 10 significantly enriched Biological Pathway (BP) GO categories were featured in a dot plot for each comparison. These top GO categories are ordered according to geneRatio which is the ratio of the input DE gene set annotated in the respective GO term. The adjusted p-value is displayed as the color of the dot and the size of the dot is determined by the Count parameter, which is the number of DE genes annotated in the respective GO term.\u003c/p\u003e\n\u003ch3\u003eBone marrow-derived macrophage (BMDM) cultures\u003c/h3\u003e\n\u003cp\u003eBMDM cultures from C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eWT\u003c/sup\u003e littermates were generated as described previously \u003csup\u003e48,49\u003c/sup\u003e. Mice were euthanized in a CO\u003csub\u003e2\u003c/sub\u003e-chamber followed by dissection of femur and tibia bones. After dissection, the bones were briefly rinsed in 70% ethanol and cold D-PBS (Sigma: 14190-169) and femur and tibia of each limb were dislodged and opened at the knee side. Next, femur and tibia bones of one limb were combined into an 18-G perforated 0.5 mL eppendorf with the open knee side facing downwards. The 0.5 mL tube was placed into a 1.5 mL eppendorf and centrifuged for 1 min at 1900g (RT) to collect the bone marrow (BM) into the 1.5 mL collection tubes. Subsequently, the resulting BM pellets were resuspended in ACK lysis buffer (1 mL/pellet) (Lonza; 10-548E) and incubated at RT for 1 min, following filtration of the cell suspension over a 70 \u0026micro;m cell strainer (VWR International: 734-0003). Cell suspensions obtained from one mouse were pooled and spun for 5 min at 1500 rpm at 4\u0026deg;C to pellet the BM cells. After counting, the BM cells were resuspended in complete DMEM medium (Gibco: 41965-062) supplemented with 10% fetal calf serum (Gibco); 1X penicillin/streptomycin (Sigma: P4333); 1X non-essential amino acids (Lonza: BE13-114E), 0.4 mM sodium pyruvate (Sigma: S8636), 2 mM L-glutamine (Lonza; BE17-605F) and 20 ng/mL murine M-CSF (Protein Service Facility, VIB), and seeded into untreated 9 cm petridishes at 4 to 5 x 10\u003csup\u003e6\u003c/sup\u003e cells/dish. Cells were maintained in standard tissue culture conditions and the medium was replaced for 50% three days after seeding, with complete DMEM medium to which murine M-CSF (40 ng/mL) was freshly added. Six days after seeding, the cells were detached using accutase solution (Sigma; A-6964), counted and reseeded in 6-well plates at 1 x 10\u003csup\u003e6\u003c/sup\u003e cells/well. One day after reseeding, the BMDM cells were lysed with TRIzol reagent (500 \u0026micro;L/well) (Life Technologies Europe; 15596018) and stored at -80\u0026deg;C until RNA isolation.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction and deflox PCR\u003c/h2\u003e \u003cp\u003eDNA was extracted from cells of interest using the ISOLATE II Genomic DNA Kit (GC Biotech BV; BIO-52066) according to the manufacturer\u0026rsquo;s instructions. DNA concentration was measured using the Nanodrop ND-1000 (Nanodrop Technologies, Thermo Scientific). As input for the deflox polymerase chain reaction (PCR), 30 ng of DNA was mixed with the correct primers (\u003cb\u003eSupplemental Table\u0026nbsp;2\u003c/b\u003e) and GoTaq Green Master Mix (Promega). Next, amplified DNA samples were separated on a 2% agarose (Life Technologies; 15510-027) gel with 5 \u0026micro;L/100mL Midori Green Advance (NIPPON Genetics) for 40 min at 160 V and visualized with Gel Doc XR+ (Biorad).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eReal-time qPCR\u003c/h2\u003e \u003cp\u003eRNA was isolated from cortex using the Aurum Total RNA Mini Kit (Bio-Rad; 732\u0026ndash;6820) according to the manufacturer\u0026rsquo;s instructions. RNA concentration was measured using the Nanodrop ND-1000 (Nanodrop Technologies, Thermo Scientific) and cDNA was prepared using the SensiFAST cDNA synthesis kit (Bioine; BIO-65054). Real-time quantitative polymerase chain reaction (RT-qPCR) was performed with the Light Cycler 480 system (Roche) using the SensiFAST SYBR No-ROX Kit (Bioline; BIO-98002). Volumes were dispensed using the I.DOT (DISPENDIX). Expression levels of the genes of interest were normalized to stable reference genes (\u003cem\u003eGapdh\u003c/em\u003e, \u003cem\u003eHprt\u003c/em\u003e, \u003cem\u003eRpl\u003c/em\u003e, \u003cem\u003eUbc\u003c/em\u003e), as determined by the geNorm Housekeeping Gene Selection Software \u003csup\u003e50\u003c/sup\u003e. Expression values were scaled as relative expression to the C3\u003csup\u003eWT\u003c/sup\u003e control condition. The primer sequences of the forward and reverse primers for the different genes are provided in \u003cb\u003eSupplemental Table\u0026nbsp;1\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eC3 ELISA\u003c/h2\u003e \u003cp\u003eHippocampal protein lysates were prepared in RIPA lysis buffer containing 50 mM Tris HCl (pH 8), 150 mM NaCl, 1% NP-40 (Tergitol), 0,5 mM EDTA (pH 8), and a Pierce Protease inhibitor tablet (Thermo Fisher Scientific; 88266) in PBS. Samples were homogenized with the Tissue Lyser II (Qiagen) at 20 Hz for 5 min, and debris was removed via centrifugation (14000g, 5 min, 4\u0026deg;C). The protein concentration of the supernatant was measured using the Pierce BCA protein assay (Thermo Fisher Scientific). A sandwich C3 ELISA was performed using the complement C3 mouse ELISA KIT (Abcam; ad157711), in accordance with the manufacturer\u0026rsquo;s protocol, loading 100 \u0026micro;g of protein lysate in the designed wells of the pre-coated antibody microtiter plate. The absorbance (450 nm) of each well was measured by VersaMax Microplate Reader (Molecular Devices) and the C3 concentration was determined using a non linear regression model in GraphPad Prism 10.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eImmunohistochemistry\u003c/h2\u003e \u003cp\u003e For paraffin sections, brains were cut into 5 \u0026micro;m slices (HM 340 E, Thermo Scientific), deparaffinized in xylene and ethanol, boiled in citrate buffer for 20 min, followed by blocking with 5% goat serum in PBS-T (PBS containing 0.3% Triton X-100) solution for 1 h at RT, and incubated with primary Abs in blocking buffer at 4\u0026deg;C ON. Primary antibody anti-GFAP (1/1000) (Dako; Z033429-2), and anti-6E10 (1/500) (BioLegend; 803001) were used. After washing with PBS, sections were stained with fluorophore-conjugated secondary Abs in PBS containing 0.1% Triton X-100 at RT for 1\u0026ndash;1.5 h. Counterstaining was done with DAPI (1/1000). For vibratome sections, 50 \u0026micro;m sections were cut using the vibratome (Leica VT1200 S). Sections were preserved at 4\u0026deg;C in 0.01% sodium azide until staining. After washing the sections in 1x PBS, a peroxidase blocking was performed, 5% hydrogen peroxide (Sigma-Aldrich; H1009) in methanol. Sections were blocked using blocking buffer containing 0.2% Triton X-100, 0.5% BSA, and 5% goat serum for 1 hour at RT. Sections were incubated with primary antibody, rabbit anti-IBA1 (1/10000) (Bio Legend; 803001) ON at RT on a horizontal shaker. Next, sections were incubated with a secondary antibody, goat-anti-rabbit biotinylated-antibody (1/1000) (Thermo Scientific; 65-6140) at RT for 1 h, with Avidin-Biotin-Complex (ABC) reagent (Vector Laboratories; PK 6100) for 30 min in the dark at RT, and with 3,3\u0026rsquo;-diaminobenzide (DAB) (Vector Laboratories; SK-4105) for 2 min at RT. Finally, the sections were dehydrated in the Leica ST5010 Autostainer XL (bidi-90% EtOH-100% EtOH-xyleen) and mounted with medium (Entellan new).\u003c/p\u003e \u003cp\u003eAll immunostainings were imaged via confocal laser scanning microscopy Zeiss LSM780 or Zeiss Axioscan Z.1. 6E10 staining was quantified in QuPath \u003csup\u003e51\u003c/sup\u003e using pixel classification with Artificial neural network (ANN_MLP). GFAP staining was quantified using ImageJ software (version 1.53c, National Institutes of Health) via consecutive despeckle, gaussian blur, Yen tresholder and particle analyzer. IBA1 stainings were uploaded to the Aiforia\u0026reg; platform (Aiforia Inc., Cambridge, MA, USA) for analysis with custom deep learning algorithms that were developed in Aiforia \u003csup\u003e52\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAβ extraction and ELISA\u003c/h2\u003e \u003cp\u003eAβ was extracted and measured as described previously \u003csup\u003e30,53\u003c/sup\u003e. For the extraction, hippocampus samples were homogenized in Tissue Protein Extraction Buffer (Thermo Scientific) supplemented with complete protease inhibitor (Therma Scientific) and phosphatase inhibitor cocktail 2 and 3 (Sigma-Aldrich) using a Qiagen Tissue Lyser II (Qiagen, 5 min, 20 Hz). The beads in the homogenized samples were spin down for 5 min at 5000g at 4\u0026deg;C. Supernatant was collected and centrifuged at 4\u0026deg;C for 1 h at 100000g (TLA-100Rotor; Beckman Coulter). Supernatant containing the soluble Aβ fraction was removed and stored at -80\u0026deg;C. The pellet was further processed in GuHCl solution containing complete protease inhibitor, sonicated, vortexed, incubated for 60 min at 25\u0026deg;C and centrifuged at 70000g for 20 min at 4\u0026deg;C. Supernatant containing insoluble Aβ was 12 times diluted with GuHCl diluent (20 mM phosphate, 0.4 M NaCl, 2 mM EDTA, 10% Block Ace, 0.2% BSA, 0.05% NaN3, 0.075% CHAPS, protease inhibitor cocktail, pH 7.0) and immediately frozen at -80\u0026deg;C.\u003c/p\u003e \u003cp\u003eTo determine the Aβ\u003csub\u003e40\u003c/sub\u003e and Aβ\u003csub\u003e42\u003c/sub\u003e levels in soluble and insoluble protein extractions, 96-well immunoplates (Maxisorp Nunc; 430314) were coated ON at 4\u0026deg;C with anti- Aβ\u003csub\u003e40\u003c/sub\u003e (1.5\u0026micro;g/mL; JRF/cAb40/28) or anti- Aβ\u003csub\u003e42\u003c/sub\u003e antibody (1.5 \u0026micro;g/mL; JRF/cAb42/46) in coating buffer (10 mM Tris\u0026ndash;HCl, 10 mM NaCl, 10 mM NaN\u003csub\u003e3\u003c/sub\u003e in 500 mL distilled H\u003csub\u003e2\u003c/sub\u003eO, pH 8.5). Plates were washed 5 times with PBST (PBS\u0026thinsp;+\u0026thinsp;0.05% Tween-20), and residual protein binding sites were blocked for 4 h at RT with 100 \u0026micro;L blocking buffer (0.1% casein buffer). 30 \u0026micro;L of either standard (Aβ\u003csub\u003e1\u0026ndash;42\u003c/sub\u003e; rPeptide or Aβ\u003csub\u003e1\u0026ndash;40\u003c/sub\u003e; rPeptide) or sample was mixed with 30 \u0026micro;L detection antibody (JRF/ABN/25 coupled to HRPO (Janssen Pharmaceutica), 1:2,000, diluted in blocking buffer). After blocking, ELISA plates were washed 5 times with PBST and 50 \u0026micro;L of the standard/sample‐detection mixtures was added to the ELISA plates. Plates were incubated ON at 4\u0026deg;C, while slowly shaking. Absorption at 450 nm was measured after adding 50 \u0026micro;L TMB substrate (BD Biosciences OptEIA\u0026trade;) followed by stopping buffer (50 \u0026micro;L 1 M H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e). The amount of Aβ was determined with GraphPad Prism 10 using a nonlinear regression model.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eA Limma-voom pipeline v3.46.0 was used to carry out DE analysis on the bulk RNA seq data. Benjamini-Hochberg correction was applied to correct the p-values for multiple testing. DE genes are genes with an adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.1 and a log\u003csub\u003e2\u003c/sub\u003eratio\u0026thinsp;\u0026gt;\u0026thinsp;0.5 or \u0026lt; -0.5.\u003c/p\u003e \u003cp\u003eAll data are represented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD). For comparison of two groups, unpaired student\u0026rsquo;s t-test was used. For comparison of multiple groups, significance was determined using two-way ANOVA with post-hoc Tukey\u0026rsquo;s multiple comparison test unless mentioned differently. All testing was two-sided. Differences were considered significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Significance levels are indicated on the graphs: *0.01 \u0026le; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **0.001\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***0.0001\u0026thinsp;\u0026le;\u0026thinsp;p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; and ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e \u003cb\u003eComplement is upregulated in microglia and astrocytes in the\u003c/b\u003e \u003cb\u003eApp\u003c/b\u003e\u003csup\u003e\u003cb\u003eNL\u0026minus;G\u0026minus;F\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emodel\u003c/b\u003e\u003c/p\u003e \u003cp\u003eBoth microglia and astrocytes have been described as key producers of complement, including C3, in the brain of several neuroinflammatory disease models \u003csup\u003e13\u0026ndash;16\u003c/sup\u003e. To examine the contribution of these glial cell types to complement-related gene expression in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model of AD, we performed bulk RNA sequencing on isolated microglia and astrocytes from 40 weeks old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice and age-matched C57BL/6J wild-type (WT) mice. Microglia were defined as CD45\u003csup\u003elow\u0026thinsp;\u0026minus;\u0026thinsp;int\u003c/sup\u003eCD11b\u003csup\u003e+\u003c/sup\u003eLy6C\u003csup\u003e\u0026minus;\u003c/sup\u003e cells and astrocytes as CD45\u003csup\u003e\u0026minus;\u003c/sup\u003eCD11b\u003csup\u003e\u0026minus;\u003c/sup\u003eO1\u003csup\u003e\u0026minus;\u003c/sup\u003eACSA2\u003csup\u003ehi\u003c/sup\u003e cells, as described previously \u003csup\u003e41\u003c/sup\u003e. To ensure purity of the sorted microglial population, Ly6C\u003csup\u003e+\u003c/sup\u003e cells were excluded to avoid contamination with pro-inflammatory microglia-like monocytes \u003csup\u003e54,55\u003c/sup\u003e. As expected, microglial marker genes (\u003cem\u003eAif1\u003c/em\u003e, \u003cem\u003eTmem119\u003c/em\u003e, \u003cem\u003eItgam\u003c/em\u003e, \u003cem\u003eP2ry12\u003c/em\u003e) were highly expressed in sorted microglia and virtually absent in the astrocytic fraction, whereas astrocytic markers (\u003cem\u003eSox9, Aldh1l1, S100b, Gfap\u003c/em\u003e) were highly expressed in sorted astrocytes and only barely detectable in microglia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). Marker genes of neurons (\u003cem\u003eTubb3\u003c/em\u003e, \u003cem\u003eDcx\u003c/em\u003e, \u003cem\u003eNefh\u003c/em\u003e), oligodendrocytes (\u003cem\u003eMog)\u003c/em\u003e, oligodendrocyte precursor cells (\u003cem\u003eSox10\u003c/em\u003e), pericytes (\u003cem\u003eSlc6a12\u003c/em\u003e) and endothelial cells (\u003cem\u003eMcam\u003c/em\u003e) were barely detectable in both sorted populations, confirming the successful and selective enrichment of microglia and astrocytes using the selected strategy. In both glial cell types, gene ontology (GO) analysis of differentially expressed genes (DEGs) identified enrichment of inflammatory pathways, with the \u0026lsquo;inflammatory response\u0026rsquo; and \u0026lsquo;immune effector process\u0026rsquo; as top enriched biological pathways in microglia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb) and astrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec), respectively. Within the microglial population, several genes associated with the disease-associated microglia (DAM) phenotype \u003csup\u003e56\u0026ndash;58\u003c/sup\u003e, including \u003cem\u003eCst7\u003c/em\u003e, \u003cem\u003eItgax\u003c/em\u003e, \u003cem\u003eClec7a\u003c/em\u003e, \u003cem\u003eCsf1\u003c/em\u003e, \u003cem\u003eAxl\u003c/em\u003e, \u003cem\u003eLpl\u003c/em\u003e, and \u003cem\u003eSpp1\u003c/em\u003e, were among the top 25 DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef), confirming the presence of these cells in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model and validating the reliability of our data. To further investigate the relative contribution of microglia and astrocytes to complement production in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model, we then focused our analysis on complement-related genes. In microglia, \u003cem\u003eMasp1\u003c/em\u003e, \u003cem\u003eC3\u003c/em\u003e, and \u003cem\u003eCfb\u003c/em\u003e were significantly upregulated, whereas \u003cem\u003eCfp\u003c/em\u003e, \u003cem\u003eCd55\u003c/em\u003e, and \u003cem\u003eCr2\u003c/em\u003e were significantly downregulated in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice compared to controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed, f). In astrocytes, the complement response appeared more robust, with multiple genes showing significant upregulation, including \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1qb\u003c/em\u003e, \u003cem\u003eC1qc\u003c/em\u003e, \u003cem\u003eC1ra\u003c/em\u003e, \u003cem\u003eC1s1\u003c/em\u003e, \u003cem\u003eC2\u003c/em\u003e, \u003cem\u003eC4b\u003c/em\u003e, \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eHc\u003c/em\u003e, \u003cem\u003eCfb\u003c/em\u003e, \u003cem\u003eCfh\u003c/em\u003e, \u003cem\u003eCd55\u003c/em\u003e, \u003cem\u003eCd59a\u003c/em\u003e, \u003cem\u003eC3ar1\u003c/em\u003e, \u003cem\u003eItgam\u003c/em\u003e, and \u003cem\u003eSerping1\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee, g). Among these, \u003cem\u003eC1qa\u003c/em\u003e stood out as one of the highest upregulated DEGs in astrocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eg). Although \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1qb\u003c/em\u003e, and \u003cem\u003eC1qc\u003c/em\u003e were also upregulated in microglia, their changes did not reach statistical significance. Despite this, microglia remained the predominant source of C1q, with log\u003csub\u003e2\u003c/sub\u003e(TPM) expression values more than double those observed in astrocytes (\u003cb\u003eFig. S1\u003c/b\u003e), consistent with their established role as the primary producers of C1q in the brain. C3, the central effector protein of the complement cascade, was significantly upregulated in both microglia and astrocytes in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model, with similar degrees of upregulation and comparable baseline expression between the two glial cell types (\u003cb\u003eFig. S1\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eMicroglial and astrocytic C3 are relevant neuroinflammatory factors\u003c/h2\u003e \u003cp\u003eTo investigate the specific contributions of microglial and astrocytic C3 in neuroinflammation and AD pathology, we generated novel conditional C3 knockout mice using Cre(ERT2)-loxP technology from ES cells acquired via EUCOMM \u003csup\u003e33\u003c/sup\u003e (\u003cb\u003eFig. S2a\u003c/b\u003e). The insertion of the loxP sites did not affect \u003cem\u003eC3\u003c/em\u003e expression, as \u003cem\u003eC3\u003c/em\u003e mRNA levels in the liver and cortex from C3\u003csup\u003efl/fl\u003c/sup\u003e mice were comparable to those from WT mice (\u003cb\u003eFig. S2b\u003c/b\u003e). The microglia-specific C3 knockout (C3\u003csup\u003emKO\u003c/sup\u003e) was generated by crossing \u003cem\u003eC3\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e mice with \u003cem\u003eCx3Cr1\u003c/em\u003e\u003csup\u003eCreERT2/+\u003c/sup\u003e mice, and astrocyte-specific C3 knockout (C3\u003csup\u003eaKO\u003c/sup\u003e) was generated by crossing \u003cem\u003eC3\u003c/em\u003e\u003csup\u003efl/fl\u003c/sup\u003e mice with \u003cem\u003eGfap\u003c/em\u003e\u003csup\u003eCre/+\u003c/sup\u003e mice. CreERT2-mediated recombination in the C3\u003csup\u003emKO\u003c/sup\u003e line was induced by administration of tamoxifen at the age of 4 weeks. Specific C3 deletion in the C3mKO and C3aKO lines was validated by FACS and subsequent deflox PCR, and \u003cem\u003eC3\u003c/em\u003e mRNA staining via RNAScope, respectively. In C3\u003csup\u003emKO\u003c/sup\u003e mice, the Cre-mediated deletion of C3 was only present in microglia from C3\u003csup\u003emKO\u003c/sup\u003e mice, and not in C3\u003csup\u003eWT\u003c/sup\u003e controls, nor in \u0026lsquo;non-microglial\u0026rsquo; brain cells or BMDM cultures from C3\u003csup\u003emKO\u003c/sup\u003e mice (\u003cb\u003eFig. S2c\u003c/b\u003e). Similarly, in astrocytes, C3 deletion was validated to be specific in the astrocytes from C3\u003csup\u003eaKO\u003c/sup\u003e mice. (\u003cb\u003eFig. S2d\u003c/b\u003e).\u003c/p\u003e \u003cp\u003eTo investigate the specific contributions of microglial and astrocytic C3 in neuroinflammation, we administered a low dose of LPS ICV to C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eaKO\u003c/sup\u003e mice and assessed how these glial-specific C3 deficiencies influence LPS-induced inflammation. First, we conducted a kinetics experiment in WT mice to compare neuroinflammatory gene expression prior to and 4 hours (h) and 3 days (d) after LPS injection. While the expression of proinflammatory genes \u003cem\u003eTnf\u003c/em\u003e, \u003cem\u003eIl1β\u003c/em\u003e, \u003cem\u003eIl6\u003c/em\u003e, and \u003cem\u003eInos\u003c/em\u003e peaked at 4 h post injection (hpi), complement genes \u003cem\u003eC3\u003c/em\u003e, \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1r\u003c/em\u003e, and \u003cem\u003eC1s\u003c/em\u003e were significantly upregulated at 3 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea), guiding our decision to focus on the 3 d timepoint for subsequent experiments. Remarkably, conditional C3 deletion in either microglia or astrocytes resulted in a significant 50% reduction in cortical C3 expression at 3 dpi (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c). Notably, the reduction in glial C3 expression also dampened the transcriptional responses of other proinflammatory genes. In C3\u003csup\u003emKO\u003c/sup\u003e mice, the expression of \u003cem\u003eTnf\u003c/em\u003e, \u003cem\u003eIl1β\u003c/em\u003e, \u003cem\u003eInos\u003c/em\u003e, microglial activation marker \u003cem\u003eAif1\u003c/em\u003e, and astrocytic activation marker \u003cem\u003eLcn2\u003c/em\u003e was significantly decreased (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). In C3\u003csup\u003eaKO\u003c/sup\u003e mice, \u003cem\u003eIl1β\u003c/em\u003e, \u003cem\u003eInos\u003c/em\u003e, and astrocytic activation markers \u003cem\u003eLcn2\u003c/em\u003e and \u003cem\u003eSerpina3a\u003c/em\u003e were significantly downregulated (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). These findings highlight the importance of both microglial and astrocytic derived C3 in mediating neuroinflammatory responses, prompting its impact in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eMicroglial and astrocytic C3 deficiency does not affect amyloidosis in the\u003c/b\u003e \u003cb\u003eApp\u003c/b\u003e\u003csup\u003e\u003cb\u003eNL\u0026minus;G\u0026minus;F\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emodel\u003c/b\u003e\u003c/p\u003e \u003cp\u003eNext, we aimed to investigate the influence of microglial and astrocytic C3 deficiency on amyloid pathology. To this end, we backcrossed C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eaKO\u003c/sup\u003e mice into the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e background. Mice were either aged to 40 weeks, or subjected to a mild peripheral inflammatory challenge via LPS injection at 22\u0026ndash;23 weeks of age followed by analysis 2 weeks later as we described before \u003csup\u003e30\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea, f). LPS administration induced an expected transient drop in body temperature and body weight and this systemic response was unaffected by either the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e background or the C3 deficiency (\u003cb\u003eFig. S3\u003c/b\u003e). Despite successful conditional deletion of C3 in microglia and astrocytes in C3\u003csup\u003emKO\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea) and C3\u003csup\u003eaKO\u003c/sup\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) mice, respectively, C3 protein levels in the hippocampus remained unchanged in both aged and LPS-treated \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003emKO\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eaKO\u003c/sup\u003e mice compared to their \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, d, g, i). Moreover, we did not detect any difference in hippocampal C3 protein levels between \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e mice and age-matched WT controls over all experimental setups. Importantly, amyloid pathology, assessed by 6E10\u003csup\u003e+\u003c/sup\u003e Aβ plaque burden, remained unchanged across genotypes in both aged mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec, h) and mice subjected to mild peripheral inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee, j). These results indicate that while glial-derived C3 is involved in driving neuroinflammatory responses, its deletion does not significantly influence amyloidosis in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model under either baseline ageing or mild peripheral inflammatory conditions.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eInducible full-body C3 deficiency does not affect pathology in the\u003c/b\u003e \u003cb\u003eApp\u003c/b\u003e\u003csup\u003e\u003cb\u003eNL\u0026minus;G\u0026minus;F\u003c/b\u003e\u003c/sup\u003e \u003cb\u003emodel\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe lack of impact of glial C3 deletion on amyloid pathology, together with our initial data showing that both microglia and astrocyte express C3, prompted us to next examine the impact of inducible full-body C3 deficiency (C3\u003csup\u003eiKO\u003c/sup\u003e). Thereto, we generated C3\u003csup\u003eiKO\u003c/sup\u003e mice by crossing the C3\u003csup\u003efl/fl\u003c/sup\u003e with \u003cem\u003eRosa26\u003c/em\u003e\u003csup\u003eCreERT2/+\u003c/sup\u003e mice and subsequent backcrossing into the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e background. The knockout in the generated \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e mice was induced at 8 weeks of age via oral gavage tamoxifen, administered daily for 5 days. This method was optimized by comparing IP and oral gavage administration of tamoxifen, with knockout efficiency assessed by deflox qPCR on gDNA from several organs and brain regions (\u003cb\u003eFig. S4\u003c/b\u003e). We observed that oral gavage administration resulted in higher knockout efficiency in the CNS compared to IP injection (\u003cb\u003eFig. S4c\u003c/b\u003e). Additionally, while IP administration of tamoxifen led to the formation of lipogranulomas in the abdomen of some mice, likely due to excess oil used to dissolve the tamoxifen \u003csup\u003e59\u003c/sup\u003e, no such adverse effects were observed in the oral gavage group (\u003cb\u003eFig. S4b\u003c/b\u003e), further supporting our choice to use oral gavage as the preferred method. Also \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e received tamoxifen to control for potential off-target effects. Resulting \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates were then again analyzed following ageing to 40 weeks (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) or after mild peripheral inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ef). The systemic response to the LPS administration was not affected by the C3 deficiency, as both \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e mice displayed the expected drop in body temperature and body weight following LPS injection, with no significant differences between genotypes (\u003cb\u003eFig. S5\u003c/b\u003e). ELISA analysis confirmed the complete removal of C3 in the hippocampus of C3\u003csup\u003eiKO\u003c/sup\u003e mice, validating the effectiveness of our inducible knockout model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, g). Further analysis revealed that amyloid plaque burden remained unchanged between C3\u003csup\u003eiKO\u003c/sup\u003e and C3\u003csup\u003eWT\u003c/sup\u003e mice in both the ageing (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) and mild peripheral inflammation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eh) conditions. Additionally, there were no significant differences in the levels of soluble or insoluble Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e and A\u003csub\u003eβ1\u0026minus;42\u003c/sub\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed, e, i, j), confirming that complete C3 deficiency did not impact amyloid deposition.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNo differences in microglial activation were observed in the hippocampus between 40 weeks old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates, as assessed by IBA1 staining and quantification of microglial number, length, and circularity (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea\u0026ndash;d). Similarly, qRT-PCR analysis of microglial activation markers (\u003cem\u003eAif1\u003c/em\u003e, \u003cem\u003eCd11b\u003c/em\u003e) in the cortex revealed no significant differences between genotypes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee, 6f). Astrocytic activation, assessed by GFAP staining in the hippocampus, also showed no differences between the groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eg\u0026ndash;h). Furthermore, qRT-PCR analysis of astrocytic activation markers (\u003cem\u003eGfap\u003c/em\u003e, \u003cem\u003eAldh1l1\u003c/em\u003e, \u003cem\u003eLcn2\u003c/em\u003e, \u003cem\u003eSerpina3a\u003c/em\u003e) in the cortex did not reveal any differences between \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eiKO\u003c/sup\u003e and \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e; C3\u003csup\u003eWT\u003c/sup\u003e littermates (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ei\u0026ndash;l). These results show that, despite the complete removal of C3 at adult age, global C3 deficiency does not influence amyloidosis or neuroinflammation in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e AD model under the conditions tested.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNeuroinflammation and complement activation are increasingly recognized as key drivers of AD pathology, yet the relative contributions of microglia- and astrocyte-derived complement remain incompletely understood. In this study, we aimed to untangle the contributions of glial-derived complement to neuroinflammation and amyloidosis in AD, particularly in the context of \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice. To this end, we performed bulk RNA sequencing on FACS-isolated microglia and astrocytes from 40 week-old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice and age-matched WT controls. Transcriptomic analyses revealed distinct gene expression profiles for both cell types, with inflammatory signaling pathways enriched in both microglia and astrocytes. Importantly, microglia and astrocytes expressed comparable levels of \u003cem\u003eC3\u003c/em\u003e mRNA which was similarly upregulated in both cell types in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e background. This finding supports a convergent activation of the complement pathway across both glial cell types, emphasizing its role as a central mediator of neuroinflammation.\u003c/p\u003e \u003cp\u003eThese observations align with growing evidence that both microglia and astrocytes serve as important producers of C3 in the CNS, although their relative contributions can vary by disease context. While microglia have long been considered the main professional phagocytes of the brain, astrocytes are increasingly recognized as active participants in neuroinflammation and even phagocytosis \u003csup\u003e28,60\u0026ndash;63\u003c/sup\u003e. For instance, in the experimental autoimmune encephalomyelitis model of multiple sclerosis \u003csup\u003e13\u003c/sup\u003e, C3 expression was primarily attributed to microglia, whereas in a Parkinson\u0026rsquo;s disease (PD) model, astrocytic C3 was identified as a key contributor to pathology \u003csup\u003e16\u003c/sup\u003e. Another PD study reported that microglia dominated C3 expression at earlier stages, while astrocytes assumed the principal role later \u003csup\u003e14\u003c/sup\u003e. Furthermore, in a tri-culture model of AD with hPSC-derived microglia, astrocytes, and neurons, C3 production required both astrocytes and microglia, with astrocytic C3 secretion triggered by microglia, but also reciprocal microglial C3 production re-induced by astrocytes \u003csup\u003e64\u003c/sup\u003e. Lastly, studies showing that C3 is a hallmark of \u0026ldquo;A1\u0026rdquo; reactive astrocytes \u003csup\u003e65\u003c/sup\u003e, a subtype implicated in various neurodegenerative diseases, reinforce the notion that glial C3 expression can be highly context dependent. Collectively, these findings highlight the complexity of glial complement activation and underscore its potentially critical role in AD pathogenesis.\u003c/p\u003e \u003cp\u003eAdditionally, while our data reaffirm the established role of microglia as the main source of C1q in the CNS, we also observed upregulation of \u003cem\u003eC1q\u003c/em\u003e in astrocytes in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model. This finding adds to evidence suggesting that astrocytic C1q expression emerges under specific pathological conditions, as illustrated by several studies. For example, Orre \u003cem\u003eet al\u003c/em\u003e., using a similar transcriptional approach on isolated microglia and astrocytes from APPswe/PS1dE9 mice, found that both astrocytes and microglia adopted a proinflammatory phenotype, but the immune alterations in astrocytes were relatively more pronounced \u003csup\u003e66\u003c/sup\u003e. Interestingly, \u003cem\u003eC1qa\u003c/em\u003e, \u003cem\u003eC1qb\u003c/em\u003e, and \u003cem\u003eC1qc\u003c/em\u003e were among the top DEGs in astrocytes but not in microglia, aligning with our dataset. Similarly, Iram \u003cem\u003eet al\u003c/em\u003e. demonstrated that aged astrocytes in the 5xFAD model displayed increased \u003cem\u003eC1q\u003c/em\u003e expression \u003csup\u003e67\u003c/sup\u003e. Their findings also suggested a functional role for astrocytic C1q in facilitating Aβ uptake. In human temporal lobe epilepsy, Aronica \u003cem\u003eet al\u003c/em\u003e. reported \u003cem\u003eC1q\u003c/em\u003e expression in astrocytes, further supporting the idea that astrocytes can upregulate \u003cem\u003eC1q\u003c/em\u003e under pathological stress \u003csup\u003e68\u003c/sup\u003e. Additionally, Ingram \u003cem\u003eet al\u003c/em\u003e. observed C1q immunolabeling in both reactive astrocytes and microglia in human multiple sclerosis tissue, particularly in plaque and peri-plaque regions \u003csup\u003e69\u003c/sup\u003e. Collectively, these studies demonstrate that while microglia remain the primary C1q source in the CNS, astrocytic C1q expression is context-dependent, emerging under specific pathological conditions. Although C3 is the central focus of this study, astrocytic C1q may add another layer of complement regulation, influencing glial crosstalk and complement-driven pathology.\u003c/p\u003e \u003cp\u003eAlthough our transcriptomic findings suggest that microglia and astrocytes both upregulate C3 in aged \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice, the functional impact of this expression remained unclear. To address this, we generated novel microglia- (C3\u003csup\u003emKO\u003c/sup\u003e) and astrocyte-specific (C3\u003csup\u003eaKO\u003c/sup\u003e) knockout models. Before disentangling the roles of microglia- and astrocyte-derived C3 in amyloid pathology, we first sought to explore their relative contributions in response to an acute neuroinflammatory stimulus, mimicked by ICV LPS injection. This allowed us to investigate glial-specific roles under inflammatory conditions, serving as a mechanistic proof-of-concept prior to extending our findings to the more complex AD-like pathology in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice. This revealed that both microglia- and astrocyte-derived C3 are significant, as conditional C3 deficiency in either cell type led to a 50% reduction in the LPS-induced increase in \u003cem\u003eC3\u003c/em\u003e expression. The remaining \u003cem\u003eC3\u003c/em\u003e expression likely originates from the other glial cell type, pointing to an equal contribution. Moreover, we observed that the expression of TLR4-induced genes, such as \u003cem\u003eTnf\u003c/em\u003e and \u003cem\u003eIl1β\u003c/em\u003e, peaked earlier at 4 hpi than complement upregulation at 3 dpi. However, in both C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eaKO\u003c/sup\u003e mice, the expression of these upstream proinflammatory genes was dampened, suggesting that complement might also crosstalk with TLR4 signaling to sustain or amplify neuroinflammation. This aligns with findings showing that complement activation amplifies TLR4-induced cytokine production \u003cem\u003ein vivo\u003c/em\u003e, through C3a and C5a receptor signaling enhancing MAPK and NF-κB activation \u003csup\u003e70\u003c/sup\u003e. This raises the possibility that complement functions in a feedback loop to modulate the TLR4 response. Alternatively, the dampened LPS response in C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eaKO\u003c/sup\u003e mice could indicate that microglia, as the brain\u0026rsquo;s primary sensors of LPS, exhibit reduced sensitivity to TLR4 activation in the absence of glial C3. These findings underscore the bidirectional interplay between complement and inflammatory signaling \u003csup\u003e71,72\u003c/sup\u003e, which are likely relevant in chronic neurodegenerative diseases such as AD. Examining the downstream pathways activated by C3 in each glial cell type, could further delineate the functional roles of microglial and astrocytic C3. For example, microglial C3 may act through autocrine or paracrine signaling to modulate phagocytosis or cytokine release, whereas astrocytic C3 could engage distinct pathways to influence microglial function. It could be of interest to cross the C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eaKO\u003c/sup\u003e mice to generate a complete glial C3 knockout mouse. This approach would allow to determine whether total \u003cem\u003eC3\u003c/em\u003e expression in the CNS is further reduced, as either glial cell type may compensate for the loss of the other. It will also provide critical insights into whether this combined knockout has a more pronounced effect on the neuroinflammatory response, helping to clarify the relative and overlapping contributions of microglia- and astrocyte-derived C3.\u003c/p\u003e \u003cp\u003eFor microglial C3 deletion, we utilized Cx3Cr1\u003csup\u003eCreERT2\u003c/sup\u003e, a widely used and highly efficient line \u003csup\u003e73\u003c/sup\u003e. Although recombination after tamoxifen administration initially affects other myeloid subsets, these cells have short lifespans and are replaced by non-recombined progenitors within weeks, ensuring stable microglial C3 deficiency \u003csup\u003e74\u003c/sup\u003e. We confirmed the absence of recombination in peripheral myeloid cells via deflox PCR on BMDM-derived cultures. Nonetheless, brain-border macrophages at the choroid plexus, meninges, or perivascular spaces could still be targeted by this strategy \u003csup\u003e73\u003c/sup\u003e. To delete C3 in astrocytes, we employed Gfap\u003csup\u003eCre\u003c/sup\u003e, which achieves broad astrocyte coverage \u003csup\u003e75,76\u003c/sup\u003e but can also drive recombination in some neural progenitor cells and neurons \u003csup\u003e77\u003c/sup\u003e. While we observed no non-astrocytic C3 deletion, minor off-target effects cannot be ruled out. More specific lines exist for each glial population: Tmem119\u003csup\u003eCreERT2\u003c/sup\u003e or P2ry12\u003csup\u003eCreERT2\u003c/sup\u003e may improve microglial specificity, albeit with lower recombination efficiency \u003csup\u003e73\u003c/sup\u003e, whereas Aldh1l1\u003csup\u003eCreERT2\u003c/sup\u003e can offer greater astrocyte specificity \u003csup\u003e78\u003c/sup\u003e but risks confounding expression in peripheral tissues \u003csup\u003e76,79\u003c/sup\u003e. Furthermore, an inducible CreERT2 approach specifically for astrocytes could provide finer temporal control, thereby avoiding potential developmental effects of C3 deficiency on the CNS and enabling a more precise understanding of how C3 mediates neuroinflammation and amyloid pathology in distinct glial populations.\u003c/p\u003e \u003cp\u003eDespite the \u003cem\u003eC3\u003c/em\u003e expression by both microglia and astrocytes, neither C3\u003csup\u003emKO\u003c/sup\u003e nor C3\u003csup\u003eaKO\u003c/sup\u003e impacted hippocampal C3 protein levels or amyloidosis in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice in ageing or following mild peripheral inflammatory challenge by LPS. The latter model was employed to mimic inflammatory exposure more relevant to human conditions, which is absent in mice housed under SPF environments \u003csup\u003e31\u003c/sup\u003e. Importantly, LPS administration induced an expected transient drop in body temperature and body weight, which was unaffected by either the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e background or the C3 genotype, ensuring a comparable inflammatory response across genotypes and providing a consistent baseline for evaluating the impact of C3 deletion in this AD model. One possibility is that non-targeted sources of C3, whether from the other glial cell type, peripheral C3, or non-glial cells, maintain overall hippocampal C3 levels. Interestingly, we also did not observe differences in hippocampal C3 protein levels between App\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e and WT mice when focusing only on the C3\u003csup\u003eWT\u003c/sup\u003e mice across all four experimental cohorts (C3\u003csup\u003eWT\u003c/sup\u003e mice from both C3\u003csup\u003emKO\u003c/sup\u003e and C3\u003csup\u003eaKO\u003c/sup\u003e lines, under both 40-week ageing and LPS-challenge paradigms), contrasting with a previous study reporting elevated C3b/iC3b levels in the brain of 9 month old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003e\u003cem\u003eNL\u0026minus;G\u0026minus;F\u003c/em\u003e\u003c/sup\u003e compared to WT mice \u003csup\u003e80\u003c/sup\u003e. The absence of detectable C3 signal in our C3\u003csup\u003eiKO\u003c/sup\u003e mice confirms the specificity of our assay, suggesting that factors such as differences in age, experimental conditions, or the C3 fragment epitope recognized by the ELISA may account for this discrepancy between studies. Regardless, these findings highlight a disconnect between transcriptomic and proteomic levels, as we did detect a significant increase in \u003cem\u003eC3\u003c/em\u003e mRNA in microglia and astrocytes of \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice compared to WT controls. Despite this upregulation of \u003cem\u003eC3\u003c/em\u003e mRNA, the lack of any effect of C3\u003csup\u003emKO\u003c/sup\u003e or C3\u003csup\u003eaKO\u003c/sup\u003e on Aβ plaque burden suggests that glial-derived C3 does not play a limiting role in amyloidosis under the tested conditions.\u003c/p\u003e \u003cp\u003eImportantly, also C3\u003csup\u003eiKO\u003c/sup\u003e mice, which effectively showed absence of C3 in the brain following tamoxifen administration, showed no altered Aβ plaque burden, soluble and insoluble Aβ\u003csub\u003e1\u0026minus;40\u003c/sub\u003e or A\u003csub\u003eβ1\u0026minus;42\u003c/sub\u003e levels, and glial activation. These results collectively indicate that C3 does not influence amyloid deposition or associated glial activation in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model of AD when its removal is established at the age of 8 weeks. These findings contrast with previous studies using non-inducible full body C3 deficient APP/PS1 mice, which reported an increase in cerebral Aβ plaques but reduction in glial activation \u003csup\u003e24\u003c/sup\u003e. One key distinction from previous studies lies in the inducible nature of the C3 knockout used here. By deleting C3 in adult mice, we circumvent its potential roles during CNS development, including synaptic pruning and early immune regulation that might otherwise influence plaque initiation or aggregation events \u003csup\u003e11,12,25,81\u003c/sup\u003e. In contrast, constitutive germline knockouts, which lack C3 from birth, may drive more pronounced changes in amyloid burden or glial responses by disrupting these early developmental processes. Indeed, complement components such as C3 are known to shape neuronal circuits, and removing them during critical developmental windows could have long-lasting effects on plaque pathogenesis and inflammation in AD. A second factor that may contribute to our lack of observed effects is the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model itself, which expresses humanized APP under endogenous regulatory elements, reducing artifacts associated with overexpression \u003csup\u003e32\u003c/sup\u003e. By contrast, APP/PS1 mice overexpress both mutant APP and presenilin 1 (PS1), potentially amplifying the impact of C3 deficiency. Moreover, the Arctic mutation (E22G) in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice enhances Aβ fibrillogenesis \u003csup\u003e82\u003c/sup\u003e and increases resistance to proteolytic degradation \u003csup\u003e83\u0026ndash;85\u003c/sup\u003e, potentially affecting how amyloid plaques interact with inflammatory pathways. A similar case has been described for the inflammasome component NLRP3, where knockout effects were observed in APP/PS1 mice \u003csup\u003e86\u003c/sup\u003e but not in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model \u003csup\u003e87\u003c/sup\u003e. Further studies are warranted to determine whether the limited impact of C3 deficiency in this study reflects the inducible timing of C3 removal, the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model background, or a combination of both. Crossbreeding constitutive C3 knockout lines with \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e or employing inducible knockouts in overexpression models such as APP/PS1 could help clarify these mechanisms and distinguish between developmental versus adult-specific roles of C3 in AD pathology.\u003c/p\u003e \u003cp\u003eSeveral considerations should be noted when interpreting these findings. While the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model offers a refined approach to studying amyloidosis, its lack of tau pathology highlights the need for future studies incorporating models that also capture this AD hallmark. Furthermore, glial heterogeneity, both spatial and functional, was not fully explored in this study, and emerging techniques like single-cell RNA sequencing and spatial transcriptomics could provide deeper insights into region-specific and subset-specific glial responses. In addition, we did not investigate synaptic integrity, as this is addressed by Singh \u003cem\u003eet al.\u003c/em\u003e (Lemere lab; unpublished results) in a complementary study, allowing us to focus on the neuroinflammatory aspects in this work. Importantly, our results on Aβ plaque load are in line with their parallel study. Lastly, while murine complement regulators differ from their human counterparts \u003csup\u003e88\u003c/sup\u003e, humanized chimeric models offer exciting opportunities to bridge this gap in future studies \u003csup\u003e89\u003c/sup\u003e. Despite these considerations, our study provides important insights into the cell-specific roles of C3 in AD. By employing conditional C3 knockout models, we demonstrated that glial C3 is a key player in neuroinflammation but does not significantly impact amyloid deposition in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e background. These results refine our understanding of complement\u0026rsquo;s role in AD, underscoring the complexity of C3-mediated pathways and the importance of further exploring targeted interventions.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eThis study demonstrates that both microglial and astrocytic C3 contribute to neuroinflammation but are insufficient to drive changes in amyloidosis in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice. The lack of effects on Aβ load and glial activation observed in inducible full-body C3 knockout mice further challenges the previously proposed roles of complement in AD pathology. Together, our findings underscore the complexity of complement signaling in neuroinflammation and AD, highlighting the need for further investigation to unravel its multifaceted roles. Conditional C3 knockout models provide a valuable platform for exploring these mechanisms and identifying novel therapeutic strategies for AD.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"323\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 104px;\"\u003e\n \u003cp\u003eAD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 219px;\"\u003e\n \u003cp\u003eAlzheimer\u0026apos;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eARIA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmyloid-related imaging abnormalities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAmyloid beta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBMDM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBone marrow-derived macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u003csup\u003eaKO\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3 astrocyte-specific knockout\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u003csup\u003eiKO\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3 inducible full-body knockout\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3\u003csup\u003emKO\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eC3 microglia-specific knockout\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ecpm\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCounts per million\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCSF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCerebrospinal fluid\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDAM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDisease-associated microglia\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDEGs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDifferentially expressed genes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003edpi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDays post injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEUCOMM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEuropean Conditional Mouse Mutagenesis Program\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFACS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFluorescence-activated cell sorting\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene ontology\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehpi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHours post injection\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eICV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIntracerebroventricular\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eKnockout\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLPS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLipopolysaccharide\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePBS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePhosphate-buffered saline\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eParkinson\u0026rsquo;s disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePS1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePresenilin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRNA-seq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRNA sequencing\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRoom temperature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSPF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSpecific pathogen-free\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTPM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTranscripts per million\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll mouse experiments complied with the current laws of Belgium (Law of 14. August 1986 related to protection and welfare of animals) and EU directive 2010/63/EU, and were approved by the animal ethics committee of Ghent University (EC 2021-003, 2023-001, 2024-035, 2024-036).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Research Foundation-Flanders (FWO Vlaanderen; 3F003118; 3F013720; 1268823N; 1295223N) and the Alzheimer Research Foundation (SAO-FRA; 20190028).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePD, CV and REV designed the research; PD, JF, RV, MB, CDN, EVW, GVI, LVH and CV performed and/or assisted with experiments. TH designed and generated the transgenic mice. PD, JF, RV, CDN, LVH, CV and REV analyzed and/or interpreted the data. PD, CV and REV wrote the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe want to thank the VIB Flow Core and VIB BioImaging Core (Ghent, Belgium) for training, support and access to the instrument park. RNA Sequencing was performed by VIB Nucleomics Core (https://nucleomicscore.sites.vib.be/). The illustrations in this manuscript were partially created with BioRender.com.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLong, J. M. \u0026amp; Holtzman, D. M. Alzheimer Disease: An Update on Pathobiology and Treatment Strategies. \u003cem\u003eCell\u003c/em\u003e \u003cstrong\u003e179\u003c/strong\u003e, 312\u0026ndash;339 (2019).\u003c/li\u003e\n\u003cli\u003eAlzheimer\u0026rsquo;s disease facts and figures. \u003cem\u003eAlzheimers Dement. J. 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T. \u003cem\u003eet al.\u003c/em\u003e NLRP3 is activated in Alzheimer\u0026rsquo;s disease and contributes to pathology in APP/PS1 mice. \u003cem\u003eNature\u003c/em\u003e \u003cstrong\u003e493\u003c/strong\u003e, 674\u0026ndash;678 (2013).\u003c/li\u003e\n\u003cli\u003eSrinivasan, S. \u003cem\u003eet al.\u003c/em\u003e Inflammasome signaling is dispensable for \u0026szlig;-amyloid-induced neuropathology in preclinical models of Alzheimer\u0026rsquo;s disease. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 1323409 (2024).\u003c/li\u003e\n\u003cli\u003eJackson, H. M. \u003cem\u003eet al.\u003c/em\u003e A novel mouse model expressing human forms for complement receptors CR1 and CR2. \u003cem\u003eBMC Genet.\u003c/em\u003e \u003cstrong\u003e21\u003c/strong\u003e, 101 (2020).\u003c/li\u003e\n\u003cli\u003eMancuso, R. \u003cem\u003eet al.\u003c/em\u003e Xenografted human microglia display diverse transcriptomic states in response to Alzheimer\u0026rsquo;s disease-related amyloid-\u0026beta; pathology. \u003cem\u003eNat. Neurosci.\u003c/em\u003e \u003cstrong\u003e27\u003c/strong\u003e, 886\u0026ndash;900 (2024).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e. Sequences of the forward and reverse primers used for deflox PCR, genotyping PCR and RT-qPCR.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"549\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"bottom\" style=\"width: 549px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotyping and deflox PCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward (5\u0026apos;-3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse1 (5\u0026apos;-3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse2 (5\u0026apos;-3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eC3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCTTAACTCAAACTCCCAGCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eCTCCAGTACGATGGTCTCTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003eGTTCAAATCCCTACTGTGCC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 416px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDeflox RT-qPCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eC3 tm1d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;AAGTATAGGAACTTCGTCGAGATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;GTGGACTGAGTTACCAGTAATTTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003eC3 exon 5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003e\u0026nbsp;ATCATAGCCAAGTGAGGATGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003e\u0026nbsp;GTACAGGAACCTGAGAGACAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 416px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRT-qPCR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eRpl\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCCTGCTGCTCTCAAGGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTGGTTGTCACTGCCTCGTACTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eHprt\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eAGTGTTGGATACAGGCCAGAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eCGTGATTCAAATCCCTGAAGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eUbc\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eAGGTCAAACAGGAAGACAGACGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTCACACCCAAGAACAAGCACA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eGapdh\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eTGAAGCAGGCATCTGAGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eCGAAGGTGGAAGAGTGGGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eTnf\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eACCCTGGTATGAGCCCATATAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eACACCCATTCCCTTCACAGAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eIl1\u0026beta;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCACCTCACAAGCAGAGCACAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eGCATTAGAAACAGTCCAGCCCATAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eIl6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eTAGTCCTTCCTACCCCAATTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTTGGTCCTTAGCCACTCCTTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eInos\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCCTCAGGGGTTATTGGACTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eGGGGACACACACTATCTCTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eC3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCCAGCTCCCCATTAGCTCTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eGCACTTGCCTCTTTAGGAAGTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eC1qa\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eAAAGGCAATCCAGGCAATATCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTGGTTCTGGTATGGACTCTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eC1r\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eGCCATGCCCAGGTGCAAGATCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTGGCTGGCTGCCCTCTGATG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eC1s\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eTGGACAGTGGAGCAACTCCGGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eGGTGGGTACTCCACAGGCTGGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eAif1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eATCAACAAGCAATTCCTCGATGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eCAGCATTCGCTTCAAGGACATA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eCd11b\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eATGGACGCTGATGGCAATACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTCCCCATTCACGTCTCCCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eLcn2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eCCAGTTCGCCATGGTATTTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eCACACTCACCACCCATTCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cem\u003eSerpina3a\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 164px;\"\u003e\n \u003cp\u003eATTTGTCCCAATGTCTGCGAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 169px;\"\u003e\n \u003cp\u003eTGGCTATCTTGGCTATAAAGGGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Antibodies used for flow cytometry and immunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"557\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibody\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLabel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDilution\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCompany\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReference number\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFlow cytometry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFixable Viability Dye\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eeFluor 506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eeBioscience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e65-0866-14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCD45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBUV805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBD Biosciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e748370\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCD11b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBUV395\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBD Biosciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e563553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eACSA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003ePE-Cy7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eMiltenyi Biotec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e130-123-284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eeFluor660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eeBioscience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e50-6506-82\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eLy6C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eeFluor450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBD Biosciences\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e553104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\" valign=\"bottom\" style=\"width: 557px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e6E10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eBiolegend\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e803001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eGFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eDako\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003eZ033429-2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eIBA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/10000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eWako Chemicals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e019-19741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAnti-rabbit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 79px;\"\u003e\n \u003cp\u003eBiotin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1/1000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 140px;\"\u003e\n \u003cp\u003eThermo Scientific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 133px;\"\u003e\n \u003cp\u003e65-6140\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration-advances","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Molecular Neurodegeneration Advances](https://mnadvances.biomedcentral.com/)","snPcode":"44477","submissionUrl":"https://submission.springernature.com/new-submission/44477/3?","title":"Molecular Neurodegeneration Advances","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Alzheimer's disease, complement, C3, neuroinflammation","lastPublishedDoi":"10.21203/rs.3.rs-6597252/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6597252/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eAlzheimer's disease (AD) is intricately linked with neuroinflammation, with the complement system, particularly C3, emerging as a critical player. However, research has been hampered by the reliance on classical germline C3 knockout and APP overexpressing mouse models, which do not allow to study temporal and cell-specific C3 effects, and do not accurately reflect the complexity of AD pathology.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this study, we investigated the impact of conditional C3 deficiency on neuroinflammation and AD pathology, by generating microglia-specific (C3\u003csup\u003emKO\u003c/sup\u003e), astrocyte-specific (C3\u003csup\u003eaKO\u003c/sup\u003e), and inducible full-body (C3\u003csup\u003eiKO\u003c/sup\u003e) C3 knockout mice. To assess the role of C3 in both acute and chronic neuroinflammation, we employed an intracerebroventricular (ICV) LPS injection model in these mice alongside studies in the \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e knock-in mouse model of AD upon aging and mild peripheral inflammation.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOur results show that complement genes, including \u003cem\u003eC3\u003c/em\u003e, are upregulated in microglia and astrocytes from 40 weeks old \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice compared to their wildtype counterparts. Both microglia and astrocytes were shown to be significant sources of C3, as conditional C3 deficiency in either cell type led to decreased C3 expression and a dampened neuroinflammatory transcriptional response following ICV LPS injection. However, microglial- and astrocytic-specific C3 deficiency in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e mice did not affect total hippocampal C3 protein levels and amyloid plaque burden upon both 40 weeks of aging and in mild peripheral inflammation conditions. Also full-body C3 knockout, induced at the age of 8 weeks, did not alter Aβ pathology and glial activation, despite the complete removal of C3.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur findings show that while C3 contributes to neuroinflammatory responses, its role in chronic AD-associated pathology is more complex than previously thought. Our study using novel cell-specific and inducible C3 knockout mice combined with the knock-in \u003cem\u003eApp\u003c/em\u003e\u003csup\u003eNL\u0026minus;G\u0026minus;F\u003c/sup\u003e model provides new insights into the cell-specific roles of complement in AD, and highlights the need for further investigation into the complement system's involvement in neurodegenerative diseases.\u003c/p\u003e","manuscriptTitle":"Glial-specific and inducible full body C3 deficiency does not affect amyloid pathology in the AppNL-G-F mouse model of Alzheimer’s disease","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-29 14:18:05","doi":"10.21203/rs.3.rs-6597252/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-22T23:06:34+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-12T15:36:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-07T00:17:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-01T15:03:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311742641031381828080240685812611238244","date":"2025-05-29T20:47:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"199310458724694846955209773107096232318","date":"2025-05-28T22:37:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"26735430051219304055573228707845153314","date":"2025-05-28T08:06:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-27T19:35:27+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-27T18:29:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-27T03:52:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Molecular Neurodegeneration Advances","date":"2025-05-05T20:26:48+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"molecular-neurodegeneration-advances","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Molecular Neurodegeneration Advances](https://mnadvances.biomedcentral.com/)","snPcode":"44477","submissionUrl":"https://submission.springernature.com/new-submission/44477/3?","title":"Molecular Neurodegeneration Advances","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c2fc2314-016d-48b5-a745-2f0b2a58aeb9","owner":[],"postedDate":"May 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-02T18:55:02+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-29 14:18:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6597252","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6597252","identity":"rs-6597252","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}