Abstract

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.

Supplementary information

   Supplementary information accompanies this paper at
   10.1186/s13024-020-00412-5.

Background

   Late-onset Alzheimer’s disease (LOAD) is the most common cause of
   dementia worldwide [[55]1]. LOAD presents as a heterogenous disease
   with highly variable outcomes. Recent efforts have been made to
   molecularly characterize LOAD using large cohorts of post-mortem human
   brain transcriptomic data [[56]2]. Systems-level analysis of these
   large human data sets has revealed key drivers and molecular pathways
   that reflect specific changes resulting from disease [[57]2, [58]3].
   These studies have been primarily driven by gene co-expression analyses
   that reduce transcriptomes to modules representing specific disease
   processes or cell types across heterogenous tissue samples [[59]2,
   [60]4–[61]6]. Similar approaches have been used to characterize mouse
   models of neurodegenerative disease [[62]7]. Detailed cross-species
   analysis reveals a translational gap between animal models and human
   disease, as no existing models fully recapitulate pathologies
   associated with LOAD [[63]8–[64]11]. New platforms to rapidly assess
   the translational relevance of new animal models of LOAD will allow
   efficient identification of the most promising preclinical models.

   In this study, we describe a novel gene expression panel to assess
   LOAD-relevance of mouse models based on expression of key genes in the
   brain. We used a recent human molecular disease catalog based on
   harmonized co-expression data from three independent post mortem brain
   cohorts (ROSMAP, Mayo, Mount Sinai Brain bank) [[65]12–[66]14] and
   seven brain regions that define 30 human co-expression modules and five
   consensus clusters derived from the overlap of those modules [[67]9].
   These modules were used to design a mouse gene expression panel to
   assess the molecular overlap between human disease states and mouse
   models. This nCounter Mouse AD panel was piloted with samples from
   three novel mouse models of LOAD. Same-sample comparison between
   NanoString and RNA-Seq data demonstrated high per-gene correlation and
   overall concordance when separately compared to human disease
   co-expression modules. Taken together, the rapid screening of mouse
   models in the course of different life stages will allow better
   characterization of models based on alignment with specific human
   molecular pathologies.

Results

Human-mouse co-expression module conservation and probe coverage across 30
LOAD associated modules

   An overview of the Mouse AD panel design for translating the 30 human
   AMP-AD co-expression modules from three cohorts and seven brain regions
   is depicted in Fig. [68]1. Mouse to human gene prioritization resulted
   in the selection of 760 key mouse genes targeting a subset of highly
   co-expressed human genes plus 10 housekeeping genes, which explained a
   significant proportion of the observed variance across the 30 human
   AMP-AD modules (Methods). Co-expression modules were grouped into
   functionally distinct consensus clusters as previously described by
   Wan, et al. (see also Table S[69]1) [[70]9]. These consensus clusters
   contain expression modules from different brain regions and independent
   studies that share a high overlap in gene content and similar
   expression characteristics. Consensus clusters were annotated based on
   Reactome pathway enrichment analysis for the corresponding genes within
   each functionally distinct cluster ([71]Methods, Table S[72]1). Since
   consensus clusters showed an enrichment of multiple biological
   pathways, the highest rank and non-overlapping Reactome pathway was
   used to refer to each cluster (Table S[73]2). In order to assess the
   conservation of sequence and gene expression levels between human and
   mouse genes for each of the 30 human co-expression modules, dN/dS
   values were correlated with the overall overlap in expression in brains
   from six-month-old C57BL/6 J (B6) mice (Fig. [74]2a). The fraction of
   orthologous genes expressed in the mouse brain, based on the presence
   or absence of transcripts at detectable levels, was very highly
   correlated with the overall module conservation (p < 2.2 × 10^− 16,
   Pearson’s correlation coefficient: − 0.96). Module conservation was
   based on the median dN/dS statistics measuring the rate of divergence
   in the coding sequence for all genes within a given module between both
   species (Figure S[75]1). Notably, human co-expression modules of
   Consensus Cluster C, associated with the neuronal system and
   neurotransmission, showed the lowest degree of sequence divergence with
   a high proportion of human genes (64–72%) expressed in six-month-old B6
   mice. In contrast to the highly conserved neuronal modules, immune
   modules of Consensus Cluster B contained genes that recently diverged
   on the sequence level and acquired a higher number of destabilizing
   missense variants. These modules showed the highest median dN/dS values
   and the lowest fraction of genes (27–46%) expressed in the mouse brain
   across all tested modules. The remaining human co-expression modules,
   associated with different functional categories (Fig. [76]2a, Table
   S[77]1), had intermediate overlap in expression levels between human
   and mice. Each of the 30 human co-expression modules was covered with
   an average of 148 NanoString mouse probes (SD = 50 probes), where a
   single mouse probe can map to multiple human modules from different
   study cohorts and across several brain regions. Overall, mouse probe
   coverage for human co-expression modules ranged between 4 and 19%,
   depending on the size and level of conservation of the targeted human
   module (Fig. [78]2b and c, Tables S[79]2 and S[80]3). For three of the
   largest human co-expression modules harboring over 4000 transcripts,
   the probe coverage was slightly below the targeted 5% coverage
   threshold. However, these large modules are predominantly associated
   with neuronal function and show a high degree of expression and
   sequence conservation between human and mouse (Figs. [81]2a). Immune
   modules, containing genes that recently diverged on the coding sequence
   level, are well covered with a median coverage of 10% (Fig. [82]2c). A
   complete annotation of mouse probes to human transcripts for each human
   co-expression module is provided in Table S[83]3. In addition, we
   compared our novel panel to the existing nCounter Mouse Neuropathology
   panel designed to assess expression changes in multiple
   neurodegenerative diseases. We observed an overlap of 105 probes (7%)
   between both panels, highlighting that most of our selected probe
   content is novel and specific to LOAD associated disease processes and
   pathways.

Fig. 1.

   [84]Fig. 1
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   Overview of the nCounter Mouse AD panel design. The novel Mouse AD
   panel measures expression of genes from a set of 30 human co-expression
   modules from three human LOAD cohorts, including seven distinct brain
   regions. Human genes central to each of the human expression modules
   were prioritized for the Mouse AD panel to select conserved signatures
   of LOAD associated pathways

Fig. 2.

   [86]Fig. 2
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   Human to mouse comparison and probe coverage summary statistics. a
   Human-mouse sequence divergence (median dN/dS values) is inversely
   correlated (Pearson’s correlation coefficient: − 0.96) with the
   fraction of genes being expressed in B6 mouse brain for each of the
   human co-expression modules. b Coverage of the 770 selected mouse
   NanoString probes for the 30 human co-expression modules associated
   with five functional consensus clusters. The size and number of human
   co-expression modules differs for the three post-mortem brain cohorts
   (ROSMAP, Mayo, Mount Sinai Brain Bank) and across the seven included
   brain regions. c This results in a varying degree of probe coverage for
   each module with a number of disease associated consensus clusters
   (a-e), reflecting disease related pathways and processes

Prioritized subset of key genes shows a higher degree of sequence
conservation and expression level across modules

   In order to assess the level of sequence divergence and expression for
   the prioritized subset of genes on the novel panel, the selected subset
   of genes was compared to all genes across the 30 human co-expression
   modules. The 760 key genes, explaining a significant proportion of the
   observed variance in each human module, showed an overall lower level
   of sequence divergence (median dN/dS values) when compared to all other
   genes in the modules (Fig. [88]3, Figure S[89]1). Furthermore, the
   selected key genes on the Mouse AD panel also displayed a higher
   average level of gene expression in brains of six-month-old B6 mice
   compared to the remaining genes for each of the 30 modules (Fig.
   [90]3). This highlights that our formal prioritization procedure
   resulted in the selection of a subset of highly expressed key genes,
   which are also more conserved between human and mouse facilitating the
   translation of co-expression profiles across species.

Fig. 3.

   [91]Fig. 3
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   NanoString Mouse AD probe genes are strongly conserved and show high
   expression levels in the mouse brain. Comparison between gene-level
   sequence divergence and transcript abundances in 6 months old B6 mouse
   brains for all genes (red) in human co-expression modules and the
   subset of 770 genes covered by NanoString probes on the Mouse AD panel
   (green). Human transcripts within AMP-AD co-expression modules targeted
   by selected mouse NanoString probes highlighted in green showed higher
   levels of sequence conservation and transcript abundance across species
   when compared to unselected genes within modules

5xFAD mouse co-expression modules correlate with AMP-AD modules enriched for
inflammatory and stress response pathways

   To validate our novel Mouse AD panel, a time-course analysis was
   performed to correlate human AMP-AD co-expression modules with the
   5xFAD mouse model carrying a transgenic insertion with five familial
   mutations in APP and PSEN1. The 5xFAD strain is a commonly used model
   of neurodegenerative disease for which neuropathology, histological as
   well as behavioral data are readily available (Fig. [93]4a). We
   analyzed mouse NanoString data from brain hemispheres from 1 to 12
   months old mice in order to identify an overlap with human post-mortem
   co-expression modules in the course of amyloid deposition and aging.
   5xFAD mice have been shown to accumulate high levels of intraneuronal
   Aβ42 at around 1.5 months of age and extracellular amyloid at around
   2 months of age in the subiculum and cortex (Fig. [94]4a) [[95]15].
   Amyloid deposition in 5xFAD mice induced rising oxidative stress in
   surrounding cells and tissues and led to subsequent cell dysfunction
   and elevation of apoptosis markers. This is reflected in the
   transcriptomic response of one to 2 months old 5xFAD mice which showed
   a significant positive correlation (p < 0.05, Pearson’s correlation
   coefficient > 0.25) with multiple human AMP-AD modules enriched for
   pathways linked to the cellular stress response in Consensus Cluster E
   (Fig. [96]4a). The 5XFAD strain also exhibited neuroinflammation after
   2 months of age, as previously demonstrated by an increase in
   astrogliosis and microgliosis after initial plaque deposition [[97]15].
   This is in line with our 5xFAD data from the Mouse AD panel which
   showed an increased correlation between 3 and 4 months with several
   human modules in Consensus Cluster B enriched for immune related
   pathways. Furthermore, we observed an increased correlation with
   inflammatory AMP-AD co-expression modules after 4 months of age, where
   older mice at (6–12 months) showed a highly significant overlap with
   human immune modules in the cerebellum, superior temporal gyrus and
   inferior frontal gyrus (p < 0.05, Pearson’s correlation
   coefficient > 0.35). This clear shift in inflammatory gene expression
   signatures after 3 months of age is a hallmark of the 5xFAD model in
   response to aggressive amyloid deposition in the brain and corresponded
   with previous findings from a transcriptomic survey of 5xFAD mice
   [[98]16]. Reactome pathway analysis in 5xFAD mice compared to
   age-matched B6 controls supported the results from our correlation
   analysis (Fig. [99]4b). At a young age (3 months), several stress and
   immune related pathways were enriched in 5xFAD mice when compared to B6
   mice. These pathways, including the activation of FOXO transcription
   factors pathway, are well known to mediate a cellular stress response
   to Aβ42 [[100]17]. In older mice between the ages of 6 to 12 months,
   pathways linked to microglia and complement activation, such as the
   DAP12 signaling pathway, were enriched in 5xFAD mice which is in line
   with a previous study (Fig. [101]4b) [[102]16]. Moreover, we observed a
   positive correlation with human AMP-AD modules enriched for neuronal
   pathways in Consensus Cluster C. This transcriptional response occurred
   after early amyloid deposition in male mice at 3 months (p < 0.05,
   Pearson’s correlation coefficient > 0.15) and after the onset of
   neuronal loss in female mice at 12 months. Taken together, our novel
   approach identified several transcriptomic signatures in responses to
   amyloid deposition in the 5xFAD model that correlated with human
   post-mortem data from different brain regions. Despite the overlap with
   several key disease processes on the transcriptome level across
   species, the 5xFAD strain does not fully recapitulate LOAD pathologies.
   In addition, the highly penetrant nature of the early-onset familial
   variants expressed on the 5xFAD background made it difficult to
   identify disease related processes independent of amyloid pathology.
   For this reason, we used our approach to elucidate the role of
   additional AD risk variants in a set of novel mouse models .

Fig. 4.

   [103]Fig. 4
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   Time-course correlation analysis between the 5xFAD mouse model and 30
   human co-expression modules using the NanoString Mouse AD panel. a The
   5xFAD mouse model shows a significant correlation with functionally
   distinct AMP-AD co-expression modules that correspond to previously
   reported phenotypes from by Oakley et al. [[105]15] and Landel et al.
   [[106]16]. Circles correspond to significant (p < 0.05) positive (blue)
   and negative (red) Pearson’s correlation coefficients for gene
   expression changes in 5xFAD mice (log fold change of strain minus age
   matched B6 mice) and human disease (log fold change for cases minus
   controls). Correlations are based on the comparison of mouse NanoString
   data to human RNA-seq expression data from the three AMP-AD cohorts for
   seven brain regions. Human co-expression modules are ordered into
   Consensus Clusters A-E [[107]9] describing major sources of AD-related
   alterations in transcriptional states across independent studies and
   brain regions. Consensus clusters are annotated based on the most
   significantly enriched and non-redundant Reactome pathway terms
   (Supplemental Tables S[108]1, S[109]2). b Reactome pathway enrichment
   analysis for multiple time points (1 month to 12 months) implicates
   multiple immune and stress-related processes in the response to amyloid
   deposition in the course of aging within the 5xFAD mouse model

Novel mouse models harboring LOAD associated risk variants correlate with
distinct AMP-AD modules in a brain region- and pathway-specific manner

   Three novel mouse models, harboring two LOAD risk alleles, (Table
   S[110]4) were used to translate co-expression profiles between human
   and mouse brain transcriptome data using our novel Mouse AD panel.
   Transcriptome analysis was performed for the APOE4 KI mouse, carrying a
   humanized version of the strongest LOAD associated risk allele
   (APOE-ε4) and the Trem2*R47H mouse, which harbors a rare deleterious
   variant in TREM2. The rare TREM2 R47H missense variant (rs75932628) has
   been previously associated with LOAD in multiple independent studies
   [[111]15]. In our RNA-Seq data, we observed that the variant impairs
   expression in the Trem2*R47H model and results in decreased expression
   of the major Trem2 isoform (ENSMUST00000024791) compared to B6 mice
   (adjusted FDR p = 4.26 × 10^− 49, logFC = − 1.25). The decreased
   expression of Trem2 was also detected with the NanoString Mouse AD
   panel (adjusted FDR p = 0.03, logFC = − 0.29). The difference in log
   fold change reduction was potentially due to mismatched dynamic range
   between platforms or differences between aligned isoforms of RNA-Seq
   reads and probe design in the Mouse AD Panel. In addition, a mouse
   model harboring both, the common and rare AD risk variants (APOE4
   KI/Trem2*R47H) was used to compare the transcriptional effects in mice
   carrying both variants to mice carrying only a single risk allele and
   B6 controls. Mouse transcriptome data for half brains was analyzed at
   different ages (2–14 months) to estimate the overlap with human
   post-mortem co-expression modules during aging. We observed specific
   overlaps with distinct disease processes and molecular pathways at
   different ages for the APOE4 KI and Trem2*R47H mouse models. At an
   early age (2–5 months), male APOE4 KI and Trem2*R47H mice showed strong
   negative correlations (p < 0.05, Pearson’s correlation
   coefficient < − 0.3) with human co-expression modules in Consensus
   Cluster E that are enriched for transcripts associated with organelle
   biogenesis and cellular stress response pathways in multiple brain
   regions (Fig. [112]5a). Furthermore, Trem2*R47H male mice showed a
   significantly negative association (p < 0.05, Pearson’s correlation
   coefficient < − 0.2) with immune related human modules in the
   superiortemporal gyrus, the inferiorfrontal gyrus, cerebellum and
   prefrontal cortex (Fig. [113]5a). This effect becomes more pronounced
   later in development, between six and 14 months, when the negative
   correlation with human immune modules is also observed in Trem2*R47H
   female mice. During mid-life, (6–9 months old age group), we observed
   an age-dependent effect for the APOE4 KI mouse in which human neuronal
   modules in Consensus Cluster C start to become positively correlated
   with the corresponding human expression modules (Fig. [114]5a).
   Interestingly, neuronal co-expression modules which are associated with
   synaptic signaling appear to be positively correlated with APOE4 KI,
   but not Trem2*R47H mice in an age dependent manner. This up-regulation
   of genes associated with synaptic signaling and a decrease of
   transcripts enriched for cell cycle, RNA non-mediated decay,
   myelination and glial development in aged mice was consistent for
   multiple brain regions and across three independent human AD cohorts.
   When compared to APOE4 KI mice, Trem2*R47H mice showed an age dependent
   decrease in genes associated with the immune response in several AMP-AD
   modules which is not observed for APOE4 KI mice (Fig. [115]5a).
   Notably, the APOE4 KI/Trem2*R47H mice showed characteristics of both
   single variant mouse models. At an early age, an overlap with both
   neuronal and immune associated human modules is observed and becomes
   more pronounced during aging.

Fig. 5.

   [116]Fig. 5
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   Correlation analysis between three novel LOAD mouse models and human
   co-expression modules reveals age-related changes in immune function. a
   Correlation analysis highlights age-related changes in Trem2*R47H,
   APOE4 KI and APOE4 KI/Trem2*R47H mice. Circles correspond to
   significant (p < 0.05) positive (blue) and negative (red) Pearson’s
   correlation coefficients for gene expression changes in mice associated
   with distinct human co-expression modules. This includes multiple
   modules linked to immune function (Consensus Cluster B) and stress
   response (Consensus Cluster E). b Pathway analysis for the Reactome and
   WikiPathway resources highlights a significant enrichment (FDR adjusted
   p < 0.05) of pathways involved in complement activation in both older
   (10–14 months) APOE4 KI and Trem2*R47H mice. c Genes encoding for
   complement component C1q show an antagonistic transcriptional effect
   between the Trem2*R47H and APOE4 KI/Trem2*R47H mice when compared to
   the humanized APOE4 knock-in model (*denotes FDR adjusted p < 0.05)

Differential expression and gene set enrichment analysis

   In order to relate the human co-expression modules to disease
   associated genes and pathways, we performed differential expression
   (DE) analysis for the three novel mouse models and the 5xFAD mouse.
   Each mouse model was compared to the B6 control to assess the overall
   transcriptomic response and the differences in DE genes across models.
   Both the APOE4 KI and the APOE4 KI/Trem2*R47H models showed a moderate
   number of DE genes compared to B6 mice (< 100) at mid-life (6–9 months)
   while the number of DE is smaller (< 20) late in life. Early in life
   (2–5 months), only one gene was found to be DE in both the humanized
   APOE4 KI and the APOE4 KI/Trem2*R47H model (Tables S5, S6). We observed
   a significant decrease of the mouse Apoe gene (adjusted FDR
   p = 1.78 × 10^− 69, logFC > − 3.5) reflecting that it was replaced by
   the human version. The Trem2*R47H model shows mostly down-regulated
   genes across all age groups, many of which are up-regulated in 5xFAD
   mice. While microglia related genes, including Tyrobp, Trem2, and
   complement components C1qa, C1qb, C1qc are highly up-regulated in 5xFAD
   mice, these genes are down-regulated in the Trem2*R47H model (Table
   S[118]6). To elucidate the role of these immune related disease genes
   on the pathway level, gene set enrichment analysis (GSEA) was performed
   for the four mouse models and resulting pathways were compared to the
   human AMP-AD data. GSEA revealed multiple immune associated pathways
   up-regulated in 5xFAD mice when compared to B6 mice. The strongest
   association was observed in aged mice (10–14 months) where three immune
   related pathways (phagosome, Chagas disease, osteoclast
   differentiation) are significantly up-regulated (adjusted
   Benjamini-Hochberg p < 0.05) in the 5xFAD model. These pathways, which
   were also up-regulated in multiple brain regions from independent
   AMP-AD cohorts (cerebellum, superior temporal gyrus, temporal cortex),
   were down-regulated in the Trem2*R47H model (Figure S[119]2). Notably,
   this neuro-protective effect of the Trem2*R47H allele was dampened in
   the presence of APOE4 on the APOE4 KI/Trem2*R47H background, which did
   not show any significant associations with immune pathways (Figure
   S[120]2). To follow up on this antagonistic effect, pathway enrichment
   analysis for differentially expressed genes between APOE4 KI and
   Trem2*R47H mice was performed using both the Reactome [[121]18] and
   WikiPathway knowledge pathway databases. Multiple pathways linked to
   immune function and specifically complement activation were
   significantly enriched (FDR adjusted p < 0.05) for genes showing
   opposite expression patterns in both mouse models late in life
   (10–14 months) (Fig. [122]5b). Among the genes that contribute
   significantly to this pathway enrichment were three members of the
   complement complex 1q (C1q), namely C1qa, C1qb, and C1qc that were also
   found to be up-regulated in the amyloidogenic 5xFAD model. These genes
   encode for the C1q complex and were significantly decreased in both the
   Trem2*R47H and APOE4 KI/Trem2*R47H model when compared to the APOE4 KI
   model (Fig. [123]5c, Table S[124]7). This is in line with a recent
   study that linked the decrease in synaptic damage and vulnerability in
   a humanized Trem2*R47H tauopathy mouse model to reduced C1q expression
   [[125]19]. Moreover, C1q accumulation had been shown to be drastically
   increased in synapses of APOE4 KI mice, when compared to APOE3 KI mice
   suggesting an important role of C1q in enhancing synaptic vulnerability
   to classical-complement-cascade mediated neurodegeneration [[126]20].
   Furthermore, expression of the C1q complex is critical for microglia
   function. Immune pathways up-regulated in 5xFAD mice and down-regulated
   in our Trem2*R47H model harbor multiple important genes (Tyrobp, Trem2,
   Ctss, and Apoe) linked to the activation of disease associated
   microglia (DAM). In order to further characterize the role of DAM
   genes, we compared DAM expression signatures based on recently
   published data single-cell transcriptomic data [[127]21] (Figure
   S[128]3). The expression signatures of 21 DAM associated genes on the
   NanoString panel supported an increased expression of DAM associated
   genes in the 5xFAD mice compared to B6 late in life (10-14 months)
   (Figure S[129]3). This increased expression of DAM genes was absent in
   the APOE4 and Trem2 models when compared to age-matched B6 mice.

Comparison between nCounter mouse AD panel and RNA-Seq data

   To assess the validity of the novel Mouse AD panel across
   transcriptomic platforms, we compared the results from the nCounter
   platform to RNA-Seq data for the same 137 mouse brain samples from
   three novel LOAD models carrying APOE4 and TREM2*R47H alleles for which
   both RNA-Seq and NanoString data was available. A correlation analysis
   was performed to compare the expression of the 770 NanoString probes
   across co-expression modules with RNA-Seq transcript expression for all
   ages, highlighting the different LOAD mouse models as independent
   variables (Fig. [130]6). For the direct comparison, between the 770
   NanoString probes with corresponding RNA-Seq transcripts, a similar
   range of correlation coefficients between human data and the three
   mouse models was observed (Fig. [131]6a). Overall, the correlation
   between the RNA-Seq and NanoString platforms were high across all age
   groups (Pearson’s correlation coefficients: 0.65–0.69) when comparing
   the subset of 760 key transcripts and 10 housekeeping transcripts
   across platforms. This demonstrates that the novel NanoString panel,
   despite the limited number of key custom probes, can achieve similar
   results when compared to high-throughput RNA-Seq data. Furthermore, the
   alignment of human and mouse modules based on the expression of all
   genes within each modules showed a weaker range of correlations when
   compared to transcripts covered by the 770 NanoString probes (Fig.
   [132]6b). Overall, we found strong and significant correlations between
   the results. Notably, these correlations generally increased with mouse
   age, suggesting that the human relevance of the models is increasing
   with age and that this relevance is captured well by both the
   NanoString and RNA-Seq platforms (Fig. [133]6b). A mild correlation at
   around 3 months of age (Pearson’s correlation coefficient: 0.39)
   increased to a moderate correlation at 12 months of age (Pearson’s
   correlation coefficient: 0.51). Furthermore, we observed a high
   correlation of log count values for the majority of NanoString probes
   when compared to log TPM transcript ratios from RNA-Seq data. The
   majority of the 770 measured NanoString probes (716/770 probes, 93%)
   were positively correlated with RNA-Seq transcripts (Figure S[134]4).
   In order to test whether noise introduced by highly variable
   transcripts affects the correlation between NanoString probes and
   RNA-Seq transcripts, Pearson’s correlation coefficients and variance in
   RNA-Seq expression across 137 samples were compared. There was no
   significant trend indicating an effect of highly variable transcripts
   on the overall correlation coefficients between transcripts measured by
   RNA-Seq and NanoString (Figure S[135]4).

Fig. 6.

   [136]Fig. 6
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   Platform comparison of how the Mouse AD Panel and RNA-Seq each
   correlate with the AMP-AD modules. Correlation coefficients for human
   AMP-AD co-expression modules and gene expression profiles derived from
   the RNA-Seq (x-axis), and the NanoString Mouse AD Panel for the same
   mouse samples (y-axis). Both data types were obtained from 137 samples,
   including three different ages and three mouse models carrying LOAD
   risk variants. a Strong positive correlations (p < 2.2 × 10^− 16) were
   observed across all ages and samples combined when comparing expression
   of the 770 transcripts on the NanoString panel. b The correlation
   between NanoString and RNA-Seq expression analysis decreased overall
   when comparing all module transcripts measured by RNA-Seq to the subset
   of 770 probes on the NanoString panel. However, an age specific effect
   was observed for the mouse transcripts in which correlation with human
   co-expression modules increased with age (3–5 months p = 4.39 × 10^− 8,
   7–9 months p = 2.35 × 10^− 8, 12–14 months p = 2.75 × 10^− 13)

Discussion

   Here, we describe a novel systems biology approach to rapidly assess
   disease relevance for three novel mouse models carrying two human risk
   variants, strongly associated with LOAD. The nCounter Mouse AD gene
   expression panel was designed to align human brain transcriptome data
   covering 30 co-expression modules. Cross-species comparison of human
   and mouse revealed that immune associated co-expression modules which
   harbor genes that have recently diverged in sequence were more likely
   to be lowly expressed or absent at the transcript level in brains from
   6 months old B6 mice. In contrast, neuronal modules containing genes
   with a lower degree of sequence divergence between both species were
   more likely to be highly and constitutively expressed in the mouse
   brain when compared to the remaining co-expression modules. This is in
   line with evidence from multiple studies highlighting that conserved
   neuronal process in the brain are under strong purifying selection
   while immune related genes are more likely to diverge in function and
   expression patterns across species [[138]22, [139]23]. By using our
   prioritization approach, we selected for key mouse genes targeting a
   subset of highly co-expressed human genes. This subset of genes on the
   NanoString panel showed overall lower levels of sequence divergence
   compared to human genes and higher expression levels in the mouse
   brain, reducing potential noise introduced by lowly expressed
   transcripts across expression modules.

   Cross-platform comparison between the novel Mouse AD panel and RNA-Seq
   data revealed a strong correlation between mouse gene expression
   changes independent of platform related effects. Notably, the
   correlation between nCounter probe and RNA-Seq transcript expression
   with human co-expression modules was highest in aged mice older than
   12 months. This age-dependent overlap might be expected due to the
   late-onset nature of Alzheimer’s disease resulting in an increased
   number of highly co-expressed genes in aged mice carrying human LOAD
   risk variants. In addition, the strongest correlation between human and
   mouse module signatures was observed when using the subset of 770
   transcripts on the NanoString panel. This highlights that assessment of
   key genes in the brain, contributing highly to module expression, can
   improve the characterization of novel LOAD mouse models and their
   alignment with specific human co-expression modules.

   The time-course analysis of the well-established 5xFAD mouse model
   using our novel panel revealed a significant overlap in transcriptional
   signatures with several human co-expression modules from distinct brain
   regions during aging. Several hallmark features of the 5xFAD model,
   such as the increased cellular stress response after early amyloid
   deposition, correlated well with human AMP-AD modules enriched for
   stress responsive transcripts. In addition, an age-related increase in
   several key inflammatory pathways and processes following more severe
   amyloid pathology was observed in 5xFAD mice. These findings
   highlighted that aged 5xFAD mice closely resemble pro-inflammatory
   transcriptional signatures of post-mortem brain samples from deceased
   LOAD patients. Interestingly, novel LOAD mouse models showed
   concordance with distinct human co-expression modules, reflecting a
   different transcriptional response driven by the human APOE and TREM2
   associated LOAD risk variants. The strong negative correlation between
   the Trem2*R47H knock-in mice and immune related human co-expression
   highlights the important role of the LOAD associated TREM2 R47H variant
   in Alzheimer’s related immune processes. This effect, which was
   reproduced across co-expression modules from multiple human brain
   regions (cerebellum, frontal cortex, temporal gyrus, frontal gyrus,
   frontal pole), was inverted in the presence of a high amyloid burden in
   the 5xFAD model. Immune related pathways containing genes linked to
   microglia activation were significantly increased in 5xFAD mice and
   decreased in Trem2*R47H mice. Interestingly, this inflammatory response
   was partially restored in the presence of APOE4 allele on the APOE4
   KI/Trem2*R47H background suggesting an interaction between the two LOAD
   risk alleles. Pathway and differential gene expression analysis
   revealed antagonistic expression signatures between APOE4 KI and
   Trem2*R47H mice linked to the activation of the classical complement
   component through C1q members (C1qa, C1qb, C1qc). C1q protein
   accumulates at senescent synapses in the course of normal brain aging
   making them more vulnerable to complement mediated neurodegeneration. A
   recent study showed that expressing the R47H risk allele in a humanized
   Trem2 mouse model lowers the expression of C1q at synapses which in
   turn protects them from damage in the setting of a tauopathy mouse
   model [[140]19]. This is in line with multiple studies which showed
   that the profound loss of synapses at the early stages of AD can be
   prevented by blocking activation of the complement cascade through C1q
   depletion in the mouse [[141]24, [142]25]. In contrast, the APOE4
   isoform increases C1q accumulation at synapses making them more
   vulnerable to degeneration when compared to the APOE2 and APOE3
   isoforms in a set of APOE KI models [[143]20]. However, C1q
   accumulation at synapses alone might not be sufficient to trigger
   synaptic loss in the aging brain. Other factors mediated by the Trem2
   R47H and APOE4 risk alleles may activate the neuroinflammatory cascade
   that leads to age-related neurodegeneration which will require further
   studies in these novel LOAD models. Furthermore, a strong negative
   correlation between co-expression modules associated with cell cycle
   and DNA repair was observed for the mouse APOE4 KI model. This overlap
   with human late-onset co-expression signatures early in life was
   observed for a number of different brain regions and is absent in
   Trem2*R47H knock-in mice. Furthermore, aged APOE4 KI mice show a strong
   overlap with several human neuronal co-expression modules enriched for
   genes that play an important role in synaptic signaling and
   myelination. Although, APOE4 KI mice lack a clear neurodegenerative
   phenotype, this age dependent shift in co-expression patterns
   associated with core LOAD pathologies points to an increased
   susceptibility to cognitive decline in aged mice. This is in line with
   several studies which have shown that cognitive deficits in APOE4
   transgenic mice develop late in life [[144]26, [145]27]. Taken
   together, these results suggest that correlating gene expression
   signatures in LOAD and FAD mouse models to disease-associated AMP-AD
   modules can identify transcriptional disruptions relevant to human
   disease, even when the models are insufficiently advanced to exhibit
   full LOAD pathology. Assessing the effects of individual and
   combinations of LOAD variants in mouse models in this way can
   potentially separate the causal co-expression modules that drive LOAD
   pathology through genetic risk factors from modules that respond to
   established pathology. Furthermore, staging such in vivo models over a
   lifetime can determine the order of events, including microglia
   activation and, ultimately, neuronal loss observed in LOAD patients.
   The Mouse AD Panel described here provides an efficient platform to
   detect these events.

Limitations of the approach

   Albeit being an excellent resource for characterizing molecular
   pathways and key drivers of disease, co-expression modules based on
   human post-mortem brain data have several limitations. As end stage
   measures, they might not reflect changes that occur early in disease
   pathogenesis. For this reason, we were unable to devise age-specific
   mouse panels that could be most informative at specific disease stages.
   However, as mouse models improve and are characterized at multiple
   ages, early transcriptomic indicators of LOAD might motivate additional
   panels corresponding to stages of pathogenesis. In addition, although a
   high concordance was observed across brain regions for the 30 modules,
   they might not cover individual or region-specific differences in
   patients in response to amyloid and tau pathology [[146]9].
   Furthermore, we used brain homogenates from our mouse models for the
   transcript comparison with different human brain regions in this study.
   Dissection of mouse brain regions to match the human studies might
   further improve the observed co-expression module correlations.

Methods

AMP-AD post-mortem brain cohorts and gene co-expression modules

   Data on the 30 human AMP-AD co-expression modules was obtained from the
   Synapse data repository
   ([147]https://www.synapse.org/#!Synapse:syn11932957/tables/; SynapseID:
   syn11932957). The modules derive from three independent LOAD cohorts,
   including 700 samples from the ROSMAP cohort, 300 samples from the
   Mount Sinai Brain bank and 270 samples from the Mayo cohort. Details on
   post-mortem brain sample collection, tissue and RNA preparation,
   sequencing, and sample QC can be found in previously published work
   related to each cohort [[148]12–[149]14]. A detailed description on how
   co-expression modules were identified can be found in the recent study
   that identified the harmonized human co-expression modules as part of
   transcriptome wide AD meta-analysis [[150]9]. Briefly, Wan, et al.
   performed library normalization and covariate adjustments for each
   human study separately using fixed/mixed effects modeling to account
   for batch effects. Among the 2978 AMP-AD modules identified across all
   tissues (DOI:10.7303/syn10309369.1), 660 modules were selected by Wan,
   et al. which showed an enrichment for at least one AD-specific
   differential expressed gene set from the meta-analysis
   (DOI:10.7303/syn11914606) in cases compared to controls. Lastly, the
   edge betweenness graph clustering method was applied to identify 30
   aggregate modules that are not only differentially expressed but are
   also replicated across multiple independent co-expression module
   algorithms [[151]9]. Among the 30 aggregate co-expression modules, five
   consensus clusters have been described by Wan, et al. [[152]9]. These
   consensus clusters consist of a subset of modules which are associated
   with similar AD related changes across the multiple studies and brain
   regions. Here, we used Reactome pathway ([153]https://reactome.org/)
   enrichment analysis to identify specific biological themes across these
   five consensus clusters. A hypergeometric model, implemented in the
   clusterProfiler R package [[154]28], was used to assess whether the
   number of selected genes associated within each set of AMP-AD modules
   defining a consensus cluster was larger than expected. All p-values
   were calculated based the hypergeometric model [[155]29]. Pathways were
   ranked based on their Bonferroni corrected p-values to account for
   multiple testing. Finally, consensus clusters were annotated based on
   the highest ranked and non-overlapping term for each functionally
   distinct cluster.

Selection of NanoString probes for the nCounter mouse AD panel

   Since NanoString gene expression panels are comprised of 770 probes
   with the option to customize 30 additional probes, we developed a
   formal prioritization procedure to identify the most representative
   genes and ensure broadest coverage across all modules (Fig. [156]1).
   Expression and transcript annotations for the 30 human co-expression
   modules were obtained via the AMP-AD knowledge portal
   ([157]www.synapse.org/#!Synapse:syn11870970/tables/). To prioritize
   probe targets for the novel Mouse AD panel, human genes were ranked
   within each of the human AMP-AD co-expression modules based on their
   percentage of variation explaining the overall module behavior. First,
   we calculated a gene ranking score by multiplying correlations of
   transcripts with the percentage of variation explained by the first
   five principal components within each of the aggregated human AMP-AD
   modules. Secondly, the sums of the resulting gene scores for the first
   five principal components were calculated and converted to absolute
   values in order to rank highly positive or negative correlated
   transcripts within each human co-expression module. As a next step,
   only human transcripts with corresponding one-to-one mouse orthologous
   genes that are expressed in whole-brain tissue samples from
   six-month-old B6 mice were retained for downstream prioritization.
   While this filter risks excluding very few genes (6/760, < 1%) that may
   only be expressed at an advanced age, we maintained high representation
   of each human LOAD module. Disease-relevant effects are therefore
   robustly captured even if a few specific genes are omitted, as no
   module was determined to have more than five unexpressed genes in
   six-month-old mice. Furthermore, we included information on drug
   targets for LOAD from the AMP-AD Agora platform
   ([158]agora.ampadportal.org), as nominated by members of the AMP-AD
   consortium (10.7303/syn2580853). A total of 30 AMP-AD drug discovery
   targets that were highly ranked in our gene ranking approach and
   nominated by multiple AMP-AD groups were included on the panel (Table
   S[159]3). Finally, ten housekeeping genes (AARS, ASB7, CCDC127, CNOT10,
   CSNK2A2, FAM104A, LARS, MTO1, SUPT7L, TADA2B) were included on the
   panel as internal standard references for probe normalization. This