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| title | Cross-species spatial transcriptomics of Alzheimer’s disease |
A data-driven evaluation of the TgCRND8 mouse model using Xenium In Situ gene expression.
Alzheimer’s disease (AD) is the most common cause of dementia worldwide, characterized by progressive memory loss, cognitive decline, and behavioral changes that ultimately impair daily life. Today, tens of millions of people live with the disease, and this number is expected to rise sharply as populations age. Beyond the personal and emotional toll on patients and families, Alzheimer’s represents a growing public health challenge, with enormous social and economic consequences. Yet despite decades of research, there is still no cure, and disease-modifying treatments remain limited.  Figure 1: Projected prevalence of Alzheimer’s disease in the United States by age group. The number of people living with Alzheimer’s disease is expected to rise sharply over the coming decades, driven primarily by population aging. Source: Alzheimer’s Association, 2020 Alzheimer's disease facts and figures - link.
At the biological level, Alzheimer’s disease is not the result of a single malfunction. Although amyloid plaques and tau tangles are its most recognizable features, the disease unfolds through widespread changes in the brain’s cellular ecosystem. Neurons, glial cells, and immune cells do not simply degenerate or activate in isolation: their abundance shifts, their molecular programs are altered, and their spatial relationships within tissue are reorganized. Together, these changes shape how the disease emerges, propagates, and ultimately disrupts brain function.
Because access to living human brain tissue is inherently limited, much of our understanding of these processes comes from animal models. Over the years, a wide range of mouse models has been developed to study Alzheimer’s disease, each designed to capture specific aspects of pathology, such as amyloid accumulation, tau-related changes, or neuroinflammatory responses. Mouse models such as TgCRND8 have been instrumental in dissecting amyloid-driven mechanisms and testing therapeutic hypotheses. At the same time, mouse and human brains differ in key ways, such as their cellular composition, transcriptional regulation, and tissue organization, meaning that not all disease features observed in mice translate directly to human patients.
This diversity of models highlights an important point: Alzheimer’s disease cannot be reduced to a single biological readout, and no model is expected to reproduce all of its facets simultaneously. Rather than treating model validity as an all-or-nothing concept, a more informative question is which dimensions of the disease a given model captures, and which it does not. In this project, we adopt this perspective by comparing TgCRND8 mouse and human Alzheimer’s brain across three complementary scales: cellular composition and activation, transcriptional programs within cell types, and local spatial organization, using spatial transcriptomics to identify both points of convergence and sources of divergence.  Figure 2: Multiscale framework for evaluating mouse model relevance in Alzheimer’s disease. Alzheimer’s disease affects the brain across multiple biological scales, from cellular composition to gene regulation and spatial organization. We assess the relevance of the TgCRND8 mouse model by comparing mouse and human Alzheimer’s brains independently at each of these scales.
To investigate Alzheimer’s disease across cellular, molecular, and spatial dimensions, we rely on spatial transcriptomics, a technology that measures gene expression directly in intact tissue while preserving spatial context. This approach makes it possible to identify cell types, quantify their transcriptional states, and examine how cells are organized relative to one another, all within the same tissue section.  Figure 3: Overview of spatial transcriptomics datasets analyzed in this study. One wild-type mouse (13.4 months), one TgCRND8 mouse (17.9 months), one human Alzheimer’s disease sample, and one healthy human control were profiled using the Xenium In Situ gene expression platform. All analyses focus on cortical tissue.
For the mouse data, we focus on the TgCRND8 model of Alzheimer’s disease, a widely used transgenic model carrying human APP with the Swedish (KM670/671NL) and Indiana (V717F) mutations. These mutations drive early and robust amyloid pathology, making TgCRND8 a popular model for studying amyloid-related mechanisms in Alzheimer’s disease. One TgCRND8 mouse (17.9 months) was compared to one wild-type control (13.4 months).
Human data consist of post-mortem cortical tissue sections from one individual diagnosed with Alzheimer’s disease and one neurologically healthy control, obtained from the Human Xenium Brain dataset provided by 10x Genomics. While limited in sample number, these data provide high-resolution spatial transcriptomic measurements that enable a detailed comparison of disease-associated cellular, molecular, and spatial features within human cortex.
Both mouse and human samples were generated using the Xenium In Situ gene expression platform, which profiles a targeted panel of genes selected to capture major cell types and disease-relevant biological pathways. Using the same spatial transcriptomics technology across species ensures comparable data structure, quality metrics, and spatial resolution, forming a solid foundation for meaningful cross-species comparison.
Comparing Alzheimer’s disease across species is inherently challenging. Mouse and human brains differ not only biologically, but also in how data are generated, annotated, and spatially sampled. Without careful alignment, apparent differences could reflect technical artifacts rather than genuine disease-related effects. A central goal of this project was therefore to transform heterogeneous Xenium outputs into a common, analysis-ready representation that supports meaningful cross-species comparison.
Spatial transcriptomics data combine molecular, spatial, and morphological information at single-cell resolution. However, not all segmented cells provide equally reliable measurements. To ensure that downstream analyses were driven by biologically meaningful signal, we first assessed data quality at the level of individual cells. Using cell-level metrics such as transcript counts, number of detected genes, cell and nuclear areas, and background signal from negative controls, we identified and removed low-confidence or poorly segmented cells. Rather than targeting subtle differences within the bulk of the data, this step primarily removed clear outliers. Transcripts were retained only if they belonged to high-quality cells, ensuring consistency between quality control and the resulting expression matrices. As a result, quality control improved data reliability while preserving the core transcriptional structure of each sample.
<iframe src="{{ site.baseurl }}/assets/plotly/qc_overview_prepost.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>Figure 4: Effect of quality control filtering across mouse and human Xenium samples. Quality control removes low-confidence cells while preserving the majority of transcriptomic signal across all samples. Comparable retention patterns between mouse and human data indicate that filtering improves data consistency without distorting biological content.
Even after quality control, direct comparison between mouse and human data is complicated by differences in cell-type annotation. Mouse cell types were provided with relatively fine granularity, whereas human cell types were inferred computationally using Harmony-based integration. These annotation schemes do not align one-to-one. To overcome this mismatch, we defined a shared broad cell-class taxonomy that captures major cortical populations common to both species, including excitatory and inhibitory neurons, astrocytes, oligodendrocyte–OPC lineage cells, and immune/microglia. By performing all cross-species comparisons at this level, we preserve biological interpretability while ensuring that mouse and human cell populations are directly comparable.
An additional challenge arises from differences in spatial coverage. Human Xenium samples consist of localized cortical tissue sections, whereas mouse samples span an entire brain hemisphere and include both cortical and non-cortical regions. Without correction, this discrepancy would introduce a strong anatomical bias. To establish a common spatial basis for comparison, analyses were restricted to the cortex in both species. In mouse samples, a cortex mask was manually defined based on tissue morphology and spatial context, and only cells located within this region were retained. Within the cortex, analyses focused on neuronal, glial, and immune populations, which are most relevant to Alzheimer’s disease pathology. Human samples were already restricted to cortical tissue by design. This step ensures that observed differences reflect disease and species effects rather than gross anatomical mismatches.
<iframe src="{{ site.baseurl }}/assets/plotly/cortex.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>Figure 5: Cortex restriction of mouse spatial transcriptomics data. Cell locations are shown before and after applying a cortex mask to the mouse Xenium sample. Excluding non-cortical regions aligns the anatomical scope of mouse data with human cortical tissue, enabling meaningful cross-species comparison.
Following quality control, cell-type harmonization, and cortical restriction, we constructed standardized cell × gene expression matrices for each subject and species. To reduce noise from extremely sparse genes, filtering was applied consistently at the species level and propagated to all subject-level matrices. Expression values were then normalized using counts per million followed by log transformation. For each species, multiple versions of the matrices were retained (raw, QC-filtered, and QC-filtered normalized) allowing the same harmonized data foundation to be reused consistently across all downstream analyses. Together, these steps produce a unified representation of mouse and human spatial transcriptomics data, enabling transparent and reproducible comparison of cellular composition, transcriptional programs, and spatial organization in Alzheimer’s disease.
Goal: RQ1 main result
Text:
- Changes in abundance of major cell classes
- Evidence of disease-related activation
- Mouse vs human comparison
Visuals
- Stacked barplots of cell-type proportions
- Activation marker expression plots
Takeaway (1–2 lines, bold)
Alzheimer’s disease alters cortical cellular composition in both species, with partially conserved but species-specific patterns.
Alzheimer’s disease is associated with widespread transcriptional changes in the brain, but the extent to which these changes are conserved across species remains unclear. In this research question, we ask whether within corresponding cell types, TgCRND8 mice and human Alzheimer’s brains exhibit similar disease-associated transcriptional programs. Rather than focusing solely on gene-by-gene overlap, which is known to be limited between mouse and human, we examine transcriptional changes within matched broad cell classes and assess convergence at both the gene and pathway levels.
We first examined disease-associated transcriptional changes within matched broad cell classes in mouse and human. Across all cell classes, human Alzheimer’s samples consistently exhibit substantially more differentially expressed genes than TgCRND8 mice. This imbalance is observed in both neuronal and glial populations, indicating that transcriptional remodeling in human disease is broader and more pronounced than in the mouse model.
<iframe src="{{ site.baseurl }}/assets/plotly/rq2_de_context_kept_de.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>Figure : Gene filtering and differential expression yield by cell class. For each broad cell class, stacked bars show the fraction of panel genes that are filtered out (expression < 5% of cells), kept but not differentially expressed, or differentially expressed (DE) in human (left) and mouse (right). Hover information reports the number of cells per condition and the number of up- and down-regulated genes used in downstream analyses.
This strong asymmetry has important consequences for cross-species comparison. When one species exhibits a much larger pool of differentially expressed genes, direct gene-by-gene overlap becomes inherently limited, even if similar biological processes are affected. As a result, gene-level concordance alone provides an incomplete and potentially misleading view of cross-species similarity.
To move beyond the limitations of gene-level comparison, we examined transcriptional changes at the pathway level, where coordinated groups of genes are interpreted as functional biological programs. Pathway enrichment analyses were performed independently within each species and cell class, allowing comparison of disease-associated processes while accounting for the strong imbalance in gene-level signal between mouse and human.
At this level, a more nuanced picture emerges. Within several cell classes, mouse and human Alzheimer’s samples share dysregulated pathways despite limited overlap at the individual gene level. These shared pathways frequently involve immune-related processes, cellular stress responses, and metabolic or homeostatic functions, suggesting that Alzheimer’s disease can perturb similar biological programs across species even when different genes drive the response.
<iframe src="{{ site.baseurl }}/assets/plotly/figuresshared_reactome_tables.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>However, pathway-level convergence is neither uniform nor global. The number of enriched pathways, their statistical strength, and their direction of regulation differ markedly between mouse and human, and these differences vary across cell classes. In some cases, pathways enriched in human Alzheimer’s samples have no counterpart in the mouse model, while in others, overlap is observed but with weaker or opposite directional signals.
<iframe src="{{ site.baseurl }}/assets/plotly/rq2_layer1_plotly_coverage_scatter.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>Building on the pathway-level convergence observed above, we next refined the analysis at the gene level by focusing on curated mouse–human orthologs, with particular attention to genes previously implicated in Alzheimer’s disease. This step asks a more stringent question: are the shared transcriptional programs observed across species driven by conserved regulation of individual disease-related genes, or does convergence primarily emerge at the level of broader biological processes?
To address this, differential expression results from mouse and human were compared within each cell class using a stepwise filtering strategy. Starting from a fixed set of curated ortholog pairs, genes were progressively filtered based on whether they were significantly differentially expressed in mouse, in human, in both species, and finally whether they were regulated in the same direction across species. Alzheimer’s disease–associated genes were tracked explicitly throughout this process.
<iframe src="{{ site.baseurl }}/assets/plotly/rq2_layer2_funnel.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>This filtering reveals a sharp and consistent collapse in gene counts across all cell classes. While many orthologous genes are detected and some are differentially expressed in either species, very few satisfy both significance and directional concordance in mouse and human. Importantly, the number of conserved genes annotated as Alzheimer’s disease–associated is extremely limited. Across all examined cell classes, only a single AD-associated gene remains after all filtering steps.
To further characterize gene-level cross-species concordance, mouse and human disease-associated log fold-changes were compared directly for orthologous Alzheimer’s disease–associated genes using quadrant scatter plots. This analysis confirms that APOE is the only gene that is both significantly differentially expressed and regulated in the same direction across species, and that this signal is restricted to astrocytes.
<iframe src="{{ site.baseurl }}/assets/plotly/rq2_layer2_quadrant_panel.html" width="100%" height="560" style="border:0; border-radius:12px;" loading="lazy"> </iframe>In both species, astrocytic APOE expression is reduced in Alzheimer’s disease, with a modest effect size in the TgCRND8 mouse model (logFC ≈ −1.0) and a markedly stronger effect in human Alzheimer’s samples (logFC ≈ −12.1). This striking difference in magnitude mirrors the broader asymmetry in transcriptional remodeling observed between species, even when the direction of regulation is conserved. APOE encodes a lipid-transport protein predominantly expressed by astrocytes in the central nervous system and represents the strongest genetic risk factor for late-onset Alzheimer’s disease in humans. Its expression is closely linked to astrocyte reactivity, neuroinflammatory responses, and amyloid-β metabolism. Importantly, altered Apoe expression has been reported repeatedly in amyloid-driven mouse models, including TgCRND8, and is thought to reflect astrocytic responses to amyloid pathology. In this context, the selective conservation of APOE across species is biologically plausible and consistent with the design of TgCRND8 as a model that primarily captures amyloid-associated disease mechanisms.
More broadly, the extreme scarcity of conserved Alzheimer’s disease–associated genes — despite robust pathway-level convergence — suggests that cross-species similarity in Alzheimer’s disease arises primarily from shared cellular programs rather than widespread conservation of individual disease risk genes. This distinction highlights the importance of multiscale analysis when evaluating the translational relevance of mouse models, and underscores that model validity may depend on which aspects of the disease are being probed.
Goal: Pathway-level insight
Text:
- Motivation for pathway analysis
- Enrichment performed within each species
- Size-normalized overlap used for comparison
Visuals
- Pathway overlap dot plots
- Heatmaps of enriched pathways by cell class
Takeaway
Some biological programs converge across species despite strong gene-level divergence.
Goal: Anchor biology
Text:
- Role of APOE in AD
- Astrocyte-specific regulation
- Stronger effect in human
Visuals
- APOE expression maps
- Cross-species logFC comparison
Takeaway
Conserved regulation of APOE highlights selective molecular overlap between TgCRND8 and human AD.
Goal: RQ3 main result
Text:
- Definition of local neighborhoods
- Cell–cell proximity and spatial interactions
- Mouse vs human comparison
Visuals
- Spatial interaction maps
- Distance / neighborhood enrichment plots
Takeaway
Alzheimer’s disease alters local cellular organization, with only partial spatial conservation across species.
Goal: Bring the three RQs together
Text:
- One short paragraph per RQ
- Emphasize partial, scale-dependent relevance
- Highlight translational implications
Visuals
- Summary matrix (RQ × conserved / divergent)
- Final schematic overview
Bulleted summary
- Cross-species relevance depends on biological scale
- Transcriptional and spatial signals are stronger in human AD
- TgCRND8 captures selected but not all AD hallmarks
Links:
- 📓 Analysis notebook
- 💻 GitHub repository