The ability to investigate therapeutic interventions in animal models of neurodegenerative diseases depends on extensive characterization of the model(s) being used. There are numerous models that have been generated to study Alzheimer’s disease (AD) and the underlying pathogenesis of the disease. While transgenic models have been instrumental in understanding AD mechanisms and risk factors, they are limited in the degree of characteristics displayed in comparison with AD in humans, and the full spectrum of AD effects has yet to be recapitulated in a single mouse model. The Model Organism Development and Evaluation for Late-Onset Alzheimer’s Disease (MODEL-AD) consortium was assembled by the National Institute on Aging (NIA) to develop more robust animal models of AD with increased relevance to human disease, standardize the characterization of AD mouse models, improve preclinical testing in animals, and establish clinically relevant AD biomarkers, among other aims toward enhancing the translational value of AD models in clinical drug design and treatment development. Here we have conducted a detailed characterization of the 5XFAD mouse, including transcriptomics, electroencephalogram, in vivo imaging, biochemical characterization, and behavioral assessments. The data from this study is publicly available through the AD Knowledge Portal.

Background Late-onset Alzheimer’s disease (LOAD) is the most common form of dementia worldwide. To date, animal models of Alzheimer’s have focused on rare familial mutations, due to a lack of frank neuropathology from models based on common disease genes. Recent multi-cohort studies of postmortem human brain transcriptomes have identified a set of 30 gene co-expression modules associated with LOAD, providing a molecular catalog of relevant endophenotypes. Results This resource enables precise gene-based alignment between new animal models and human molecular signatures of disease. Here, we describe a new resource to efficiently screen mouse models for LOAD relevance. A new NanoString nCounter® Mouse AD panel was designed to correlate key human disease processes and pathways with mRNA from mouse brains. Analysis of the 5xFAD mouse, a widely used amyloid pathology model, and three mouse models based on LOAD genetics carrying APOE4 and TREM2*R47H alleles demonstrated overlaps with distinct human AD modules that, in turn, were functionally enriched in key disease-associated pathways. Comprehensive comparison with full transcriptome data from same-sample RNA-Seq showed strong correlation between gene expression changes independent of experimental platform. Conclusions Taken together, we show that the nCounter Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to assess disease relevance of potential LOAD mouse models.

Alzheimer’s disease (AD) is characterized by synaptic loss, which can result from dysfunctional microglial phagocytosis and complement activation. However, what signals drive aberrant microglia-mediated engulfment of synapses in AD is unclear. Here we report that secreted phosphoprotein 1 (SPP1/osteopontin) is upregulated predominantly by perivascular macrophages and, to a lesser extent, by perivascular fibroblasts. Perivascular SPP1 is required for microglia to engulf synapses and upregulate phagocytic markers including C1qa, Grn and Ctsb in presence of amyloid-β oligomers. Absence of Spp1 expression in AD mouse models results in prevention of synaptic loss. Furthermore, single-cell RNA sequencing and putative cell–cell interaction analyses reveal that perivascular SPP1 induces microglial phagocytic states in the hippocampus of a mouse model of AD. Altogether, we suggest a functional role for SPP1 in perivascular cells-to-microglia crosstalk, whereby SPP1 modulates microglia-mediated synaptic engulfment in mouse models of AD.

Late-onset Alzheimer’s disease (AD; LOAD) is the most common human neurodegenerative disease, however, the availability and efficacy of disease-modifying interventions is severely lacking. Despite exceptional efforts to understand disease progression via legacy amyloidogenic transgene mouse models, focus on disease translation with innovative mouse strains that better model the complexity of human AD is required to accelerate the development of future treatment modalities. LOAD within the human population is a polygenic and environmentally influenced disease with many risk factors acting in concert to produce disease processes parallel to those often muted by the early and aggressive aggregate formation in popular mouse strains. In addition to extracellular deposits of amyloid plaques and inclusions of the microtubule-associated protein tau, AD is also defined by synaptic/neuronal loss, vascular deficits, and neuroinflammation. These underlying processes need to be better defined, how the disease progresses with age, and compared to human-relevant outcomes. To create more translatable mouse models, MODEL-AD (Model Organism Development and Evaluation for Late-onset AD) groups are identifying and integrating disease-relevant, humanized gene sequences from public databases beginning with APOEε4 and Trem2*R47H, two of the most powerful risk factors present in human LOAD populations. Mice expressing endogenous, humanized APOEε4 and Trem2*R47H gene sequences were extensively aged and assayed using a multi-disciplined phenotyping approach associated with and relative to human AD pathology. Robust analytical pipelines measured behavioral, transcriptomic, metabolic, and neuropathological phenotypes in cross-sectional cohorts for progression of disease hallmarks at all life stages. In vivo PET/MRI neuroimaging revealed regional alterations in glycolytic metabolism and vascular perfusion. Transcriptional profiling by RNA-Seq of brain hemispheres identified sex and age as the main sources of variation between genotypes including age-specific enrichment of AD-related processes. Similarly, age was the strongest determinant of behavioral change. In the absence of mouse amyloid plaque formation, many of the hallmarks of AD were not observed in this strain. However, as a sensitized baseline model with many additional alleles and environmental modifications already appended, the dataset from this initial MODEL-AD strain serves an important role in establishing the individual effects and interaction between two strong genetic risk factors for LOAD in a mouse host.

30Background: Late-onset Alzheimer's disease (LOAD) is the most common form of 31 dementia worldwide. To date, animal models of Alzheimer's have focused on rare 32 familial mutations, due to a lack of frank neuropathology from models based on 33 common disease genes. Recent multi-cohort studies of postmortem human brain 34 transcriptomes have identified a set of 30 gene co-expression modules associated with 35 LOAD, providing a molecular catalog of relevant endophenotypes. Results: This 36 resource enables precise gene-based alignment between new animal models and 37 human molecular signatures of disease. Here, we describe a new resource to efficiently 38 screen mouse models for LOAD relevance. A new NanoString nCounter® Mouse AD 39 panel was designed to correlate key human disease processes and pathways with 40 mRNA from mouse brains. Analysis of three mouse models based on LOAD genetics, 41 carrying APOE4 and TREM2*R47H alleles, demonstrated overlaps with distinct human 42 AD modules that, in turn, are functionally enriched in key disease-associated pathways. 43 Comprehensive comparison with full transcriptome data from same-sample RNA-Seq 44 shows strong correlation between gene expression changes independent of 45 experimental platform. Conclusions: Taken together, we show that the nCounter 46 Mouse AD panel offers a rapid, cost-effective and highly reproducible approach to 47 assess disease relevance of potential LOAD mouse models.48 49 50 51 3 BACKGROUND 52Late-onset Alzheimer's disease (LOAD) is the most common cause of dementia 53 worldwide (1). LOAD presents as a heterogenous disease with highly variable 54 outcomes. Recent efforts have been made to molecularly characterize LOAD using 55 large cohorts of post-mortem human brain transcriptomic data (2). Systems-level 56 analysis of these large human data sets has revealed key drivers and molecular 57 pathways that reflect specific changes resulting from disease (2,3). These studies have 58 been primarily driven by gene co-expression analyses that reduce transcriptomes to 59 modules representing specific disease processes or cell types across heterogenous 60 tissue samples (2,4,5). Similar approaches have been used to characterize mouse 61 models of neurodegenerative disease (6). Detailed cross-species analysis reveals a 62 translational gap between animal models and human disease, as no existing models 63 fully recapitulate pathologies associated with LOAD (7,8). New platforms to rapidly 64 assess the translational relevance of new animal models of LOAD will allow efficient 65 identification of the most promising preclinical models. 66In this study, we describe a novel gene expression panel to assess LOAD-relevance of 67 mouse models based on expression of key genes in the brain. We used a recent human 68 molecular disease catalog based on harmonized co-expression data from three 69 independent post mortem brain cohorts (ROSMAP, Mayo, Mount Sinai Brain bank) (9-70 11) and seven brain regions that define 30 human co-expression modules and five 71 consensus...