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 1. nature
 2. molecular psychiatry
 3. articles
 4. article

Single-cell spatial transcriptomics reveals distinct patterns of dysregulation
in non-neuronal and neuronal cells induced by the Trem2R47H Alzheimer’s risk
gene mutation
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 * Article
 * Open access
 * Published: 05 August 2024


SINGLE-CELL SPATIAL TRANSCRIPTOMICS REVEALS DISTINCT PATTERNS OF DYSREGULATION
IN NON-NEURONAL AND NEURONAL CELLS INDUCED BY THE TREM2R47H ALZHEIMER’S RISK
GENE MUTATION

 * Kevin G. Johnston1,
 * Bereket T. Berackey1,2,
 * Kristine M. Tran3,
 * Alon Gelber4,
 * Zhaoxia Yu5,6,
 * Grant R. MacGregor7,8,
 * Eran A. Mukamel  ORCID: orcid.org/0000-0003-3203-95354,
 * Zhiqun Tan  ORCID: orcid.org/0000-0002-2235-45771,6,8,9,
 * Kim N. Green  ORCID: orcid.org/0000-0002-6049-67443,8 &
 * …
 * Xiangmin Xu  ORCID: orcid.org/0000-0002-5828-15331,2,6,8 

Show authors

Molecular Psychiatry (2024)Cite this article

 * 3764 Accesses

 * 64 Altmetric

 * Metrics details


ABSTRACT

The R47H missense mutation of the TREM2 gene is a known risk factor for
development of Alzheimer’s Disease. In this study, we analyze the impact of the
Trem2R47H mutation on specific cell types in multiple cortical and subcortical
brain regions in the context of wild-type and 5xFAD mouse background. We profile
19 mouse brain sections consisting of wild-type, Trem2R47H, 5xFAD and Trem2R47H;
5xFAD genotypes using MERFISH spatial transcriptomics, a technique that enables
subcellular profiling of spatial gene expression. Spatial transcriptomics and
neuropathology data are analyzed using our custom pipeline to identify plaque
and Trem2R47H-induced transcriptomic dysregulation. We initially analyze cell
type-specific transcriptomic alterations induced by plaque proximity. Next, we
analyze spatial distributions of disease associated microglia and astrocytes,
and how they vary between 5xFAD and Trem2R47H; 5xFAD mouse models. Finally, we
analyze the impact of the Trem2R47H mutation on neuronal transcriptomes. The
Trem2R47H mutation induces consistent upregulation of Bdnf and Ntrk2 across many
cortical excitatory neuron types, independent of amyloid pathology. Spatial
investigation of genotype enriched subclusters identified spatially localized
neuronal subpopulations reduced in 5xFAD and Trem2R47H; 5xFAD mice. Overall, our
MERFISH spatial transcriptomics analysis identifies glial and neuronal
transcriptomic alterations induced independently by 5xFAD and Trem2R47H
mutations, impacting inflammatory responses in microglia and astrocytes, and
activity and BDNF signaling in neurons.


SIMILAR CONTENT BEING VIEWED BY OTHERS


INTEGRATIVE IN SITU MAPPING OF SINGLE-CELL TRANSCRIPTIONAL STATES AND TISSUE
HISTOPATHOLOGY IN A MOUSE MODEL OF ALZHEIMER’S DISEASE

Article 02 February 2023


SINGLE-CELL MULTIREGION DISSECTION OF ALZHEIMER’S DISEASE

Article Open access 24 July 2024


A SINGLE-CELL ATLAS OF ENTORHINAL CORTEX FROM INDIVIDUALS WITH ALZHEIMER’S
DISEASE REVEALS CELL-TYPE-SPECIFIC GENE EXPRESSION REGULATION

Article 25 November 2019


INTRODUCTION

Genome-wide association studies (GWAS) have identified multiple genetic variants
associated with Alzheimer’s Disease (AD) [1]. One key discovery identified in
the TREM2 (Triggering receptor expressed on myeloid cell 2) gene is the R47H
missense variant, a strong risk factor for development of Late-Onset Alzheimer’s
Disease (LOAD) [2, 3]. TREM2 is an immunomodulatory cell surface receptor
expressed primarily in microglia in the brain [4, 5], and is activated by a
variety of ligands including amyloid-beta (Aβ), APOE, and phospholipids [6]. The
R47 residue of TREM2 is located within a poly-basic region of the extracellular
Ig-like domain, and may modify interactions of TREM2 with its associated ligands
[7, 8].

In AD, microglia exhibit an inflammatory response to Aβ plaques both in human AD
brains and in animal disease models [9, 10]. Evidence increasingly implicates
regulation of microglia activation in several AD-related processes including
plaque formation and growth [11], plaque compaction [11, 12], protection against
dystrophic neurites [13], regulation of development and spread of Tau pathology
[14], destruction of perineuronal nets [15, 16], and synaptic and neuronal loss
[15, 17,18,19,20], though the role of microglia in suppressing or aggravating
impacts of AD is currently unclear, and may vary with disease progression [21].

Recent efforts have produced a mouse model of the Trem2R47H mutation in which
bulk RNA-seq analysis has identified a unique Trem2R47H induced interferon
signature present at 12 months of age, believed to be associated with microglia
in response to Aβ pathology [22]. Activation of microglia significantly impacts
neuronal function and can be neurotoxic [23], but resolving the cell
type-specific impacts of this mutation on neuronal populations requires
single-cell analysis. Additionally, proximity to Aβ plaques directly impacts
glial activation and neuronal transcriptomes [24], requiring spatial
transcriptomics to analyze the combined influence of these effects.

To analyze the impacts of the Trem2R47H mutation in the context of plaque
pathology, we utilized the 5xFAD mouse model, noted for exhibiting strong Aβ
pathology at relatively early ages [25]. The 5xFAD mouse model has been
comprehensively evaluated for preclinical testing applications [26, 27]. It has
been shown that different brain regions (i.e. cortex and hippocampus) in the
5xFAD model have both common and unique gene expression responses to the
pathology, and that these changes recapitulate the human AD brain with increased
age [27]. Hemizygous 5xFAD/homozygous Trem2R47H (Trem2R47H; 5xFAD) mice enable
transcriptomic analysis of concerted Trem2R47H and 5xFAD induced patterns of
transcriptomic dysregulation across the brain.

In this study, we utilized MERFISH (Multiplexed Error-Robust Fluorescence In
Situ Hybridization) on wild-type (WT), 5xFAD, Trem2R47H, and Trem2R47H; 5xFAD
mice to probe spatial gene expression in single cells [28, 29]. MERFISH utilizes
multiplexed fluorescence in situ hybridization to identify individual RNA
transcripts at subcellular resolution, enabling spatial transcriptomic analysis
of cell transcriptomes in relation to their spatial environment.

Previous studies using MERFISH have analyzed spatial transcriptomics of
neurodegeneration in aging [30], and in microglia activation [31], proving the
efficacy of MERFISH in investigating transcriptomic dysregulation in the brain.
However, studies analyzing spatial impacts of Alzheimer’s disease in mouse
models have been limited either in cell type-specific impact analysis [32],
spatial resolution [33], or the size and number of imaged regions [24]. In
contrast, we present here a single-cell resolution, spatial transcriptomic atlas
of 5xFAD and Trem2R47H induced alterations across whole mouse coronal sections.
Our findings reveal spatially localized cell type-specific plaque and Trem2R47H
induced transcriptome dysregulations in both glia and neurons in multiple
cortical and subcortical brain regions.


MATERIALS AND METHODS


ANIMALS

The Trem2R47H mice used in this study were derived from the same Trem2R47H NSS
(Normal Splice Site) mouse colony with C57BL/6 J background as previously
reported (Jax stock #034036 [22]). This same genetic background was used for all
animals in this experiment. All animals were bred and raised by the Transgenic
Mouse Facility at UCI under a regular light/dark (12 h/12 h) cycle with ad
libitum access to food and water. All animal care and related experimental
procedures were conducted following the highest ethical standards and were
approved by the UC Irvine Institutional Animal Care and Use Committee.


PREPARATION OF MOUSE BRAIN SECTIONS AND MERFISH

Mice (wild-type, Trem2R47H, 5xFAD, and 5xFAD; Trem2R47H) were euthanized at 12
months of age via carbon dioxide inhalation followed by transcardiac perfusion
with chilled phosphate-buffered solution (PBS, pH7.2). Brains were quickly
collected with hemispheres bisected along the midline and separately embedded
with another hemisphere from the designated genotype pair (WT with 5xFAD and
Trem2R47H with 5xFAD;Trem2R47H) in a square tissue mode with Tissue-Tek® OCT
mounting medium. Each pair of brains was flash frozen in dry ice-chilled
isopentane and stored at −80 °C until cutting.

To prepare cryosections for MERFISH, two hemisphere OCT blocks containing 4
samples were combined and sectioned at −20 °C on a Leica CM1850 cryostat. A
10-µm-thick coronal slice containing both hippocampus and subiculum regions was
collected onto a specially coated 4cm-diameter coverslip (Vizgen Item#
10500001), fixed in 4% paraformaldehyde in PBS in a 6 cm petri dish, and stored
in 70% ethanol at 4 °C until MERFISH probe hybridization after a brief rinse
with PBS.

MERFISH was performed according to Vizgen’s protocol. Briefly, merslides with
mouse brain sections were rinsed with Vizgen Sample Preparation Buffer (SPB)
after the removal of 70% ethanol, incubated with Vizgen’s Formamide Wash Buffer
(FWB, 30 min at 37 °C), hybridized with a customized mouse gene panel containing
specific binary-coded probes for selected 300 mouse genes (VZG171, Supplementary
Table 1) in a parafilm-sealed plate ( ~ 40 h at 37 °C), and washed with FWB
twice (30 min at 47 °C). After the aspiration of FWB, the brain sections were
embedded with Vizgen gel mix, incubated in the clearing mix (5 mL with 50 μL
protease K, overnight at 37 °C), stained with Vizgen DAPI-poly(T) reagent from
the Vizgen 300-gene imaging kit (10 min at RT) after rinse with SPB, and washed
with FWB (10 min at RT). Subsequently, the merslide was thoroughly rinsed with
SPB before being carefully assembled into a gasket chamber.

Once assembled, each merslide was then uploaded into the MERSCOPE for imaging.
The MERFISH imaging was done on the MERSCOPE with a Vizgen 300-gene imaging kit
after the addition of the imaging buffer activator and RNase inhibitor (100 μL)
as stated in the manual. The imaging process was controlled by the Vizgen
MERSCOPE program (Software version 231.220726.1530b) with the default settings
(both polyT and DAPI channels “on”, scan thickness: 10 μm). Once MERFISH imaging
process was completed, the output files were transferred for in-depth analysis
with MERFISH Visualizer and our customized bioinformatic pipeline.

Imaging occurred in 5 total batches: (1) one 5xFAD animal (female) and one WT
animal (female), two brain slices (technical replicates) each; (2) one
Trem2R47H; 5xFAD animal (female) and one Trem2R47H animal (male), two brain
slices each; (3–5) one slice from each genotype, no duplicated animals. All
animals in batch 3 through 5 were male. The WT sample in batch 5 failed imaging
QC and was not included. The failed sample exhibited low transcripts per field
of view (FOV) counts (cutoff 10,000), the hippocampal and cortical regions were
cut off (e.g. were outside the imaged region), and most of the cortex and
hippocampus cells that remained exhibited low total gene counts (cutoff 25 per
cell). These criteria were established after production of the first batch of
samples, in conjunction with other experiments, based on computed requirements
for cell type delineation. Samples from technical replicates were aggregated for
subsequent analysis.

All slices were taken within a 0.7 mm range on the anterior-posterior axis,
centered at −2.7 mm relative to bregma. Higher precision was not possible while
retaining the ability to mount multiple slices on the same merslide, a
requirement for accurate differential expression due to batch effects.

No statistical methods were utilized to compute sample sizes, but our sample
sizes are comparable or larger than other similar experiments [24]. Researchers
were not blinded to mouse genotype.

The gene panel was constructed based on the commercially available 300-gene set
from Vizgen, and it contains markers for high resolution identification of
individual cell types, with emphasis on learning and memory associated genes in
cortical and hippocampal cell types, activated glial markers associated with
disease associated microglia, and disease associated astrocytes.


VERIFICATION OF AΒ PLAQUES BY CO-STAINING WITH THIOFLAVIN S AND 6E10 ANTIBODY

Once the MERFISH imaging process was completed, validation merslides were washed
in 100% formamide for 15 min to remove fluorescent readout probes, rinsed with
1xPBS, and stained with 6E10 mouse anti- Aβ1–16 monoclonal antibody (BioLegend
#SIG-39320, 1:500 dilution, 2 h at RT) followed by PBS washes (5 min x3) and
co-staining with 0.5% thioflavin S, and AlexFluor594-tagged goat anti-mouse IgG
(ThermoFisher #A-11005, 1:600 dilution), and DAPI (10μg/ml) for 30 min at room
temperature. Then the merslides were re-assembled into a gasket chamber and
immediately imaged using the Merscope Verification mode with the default
settings for protein staining (DAPI, anti-mouse, and anti-rabbit channels “on”,
scan thickness: 10 μm).


PROCESSING OF MERFISH DATASETS

Following automated transcript decoding and error correction via the MERSCOPE
software pipeline, individual cells were segmented using the machine learning
model cellpose [34], which was custom trained on DAPI stained slices collected
previously, captured at the same resolution. Next, transcripts were assigned to
individual cells. Cells exhibiting volume more than 1800 µm3, or less than 50
transcripts per cell were removed.

Data was then processed using our Scanpy [35] based custom pipeline, namely,
library size normalization, log transformation, regression of sequencing depth
per cell as a confounding variable, standard scaling, PCA transformation, batch
integration using harmony (integrated by batch but not genotype) [36], and UMAP
dimensionality reduction. Next, marker genes were computed (scanpy’s
sc.pl.rank_genes_groups function) and matched to cell type reference atlases
including the Allen institute cortex and hippocampus dataset [37], and the
mousebrain.org single-cell reference atlas [38]. Cell type annotations were
refined and corrected using spatial coordinates, particularly ensuring
cortical-layer-specific neuron types were correctly organized. As part of the
quality control process, cell types were subclustered, and cell subclusters
exhibiting markers for other (typically glial) cell types were excluded from
analysis as contaminated.

Region annotation of major spatial domains was performed using a semisupervised
approach based on superposition and manual annotation of cell types on the
appropriate Allen mouse brain coronal atlas slice [39], guided by the spatially
localized cell types for fine region selection, particularly in hippocampus.

Subclustering was performed by subsetting to the desired cell type and running
the same pipeline on the individual cell types. Subclusters containing less than
5% of the total cells were excluded from analysis. Markers for identified
subtypes were identified using sc.pl.rank_genes_groups. Genotype bias was
computed by first normalizing the number of cells in each subcluster by the
total number of cells contributed from that genotype, and then normalizing by
genotype, to obtain cell type proportions in each subcluster. The following
thresholds were used to identify genotype bias: if a single genotype proportion
for a given subtype exceeded 33% (25% being uniform distribution), the subtype
was considered upregulated in that genotype. Additionally, if the combined
proportion of two genotypes exceeded 60% (50% being uniform distribution), the
subtype was considered upregulated in that pair of genotypes. This latter was
restricted to identify only Trem2R47H, and 5xFAD specific upregulation (e.g. any
subtypes co-upregulated in WT and Trem2R47H; 5xFAD were not considered for
further analysis).


PSEUDOBULK DIFFERENTIAL EXPRESSION ANALYSIS

Cells were divided by cluster, genotype and batch. Genes present in fewer than
15% of cells were not analyzed for differential expression, due to limited
accuracy of differential expression in low frequency genes [40]. Data for each
cell type was aggregated by genotype and batch to construct pseudobulk
replicates [41]. Samples with fewer than 50 cells of a given cell type were
removed.

Due to unbalanced genotype proportions in individual batches, pairwise
differential expression was performed separately for each genotype pair studied.
Subsetting pseudobulk replicates to those associated with the compared
genotypes, we utilized a linear mixed effects model (lme4 and multcomp packages
[42, 43]), with batch as the random effect. Gene significance was identified
using an absolute log fold change of 0.35, and an adjusted p-value of 0.05. The
Benjamini-Hochberg method [44] was used for p-value correction. Fold changes
were computed based on the inferred values by the LME model. Log fold changes
were computed using a base of 2, unless otherwise stated.


CONTINUOUS PLAQUE PROXIMITY DIFFERENTIAL EXPRESSION

Continuous plaque proximal differential expression was computed at the
single-cell level for all cell types. Omitting the conversion to pseudobulk
profiles, DESeq2 [45] was utilized to analyze differential expression contiguous
to plaques, by including distance to plaque as a continuous covariate, and
computing differential expression as a function of distance to plaque. Genotype
was not used as factor in the model due to low sample numbers, and only 5xFAD
and Trem2R47H; 5xFAD samples were utilized in this analysis. Only adjusted
p-value (< 0.05) was used for identification of differentially expressed genes
in this analysis.


DIFFERENTIAL EXPRESSION BETWEEN REGIONS

To perform differential expression of glial cell types between different spatial
regions in the same genotype, we utilized a pseudobulk approach. After
subsetting to individual cell types and removing genes expressed in <15% of
cells, we excluded sample-region combinations in which fewer than 50 cells are
identified. We note that this completely excludes OPC analysis in the dentate
gyrus due to lack of cells. At this point, we created pseudobulk samples for
each sample-region combination (typically 10 pseudobulk samples for each slice,
based on the 10 annotated regions). We then computed differential expression
within the same genotype, comparing expression in each region to the average
expression in all other regions, to identify spatially variable genes. We
utilized a linear mixed effects model, with batch as the random effect, and gene
significance was identified using an absolute log fold change of 0.35, and a
p–value of 0.05.


CELLPOSE SEGMENTATION OF PLAQUES

Initial visualization of DAPI staining in merslides appeared to show plaque
staining in addition to cell nuclei. We confirmed this on MERFISH-imaged 5xFAD
merslides by costaining with DAPI, thioflavin S, and 6E10 antibody against Aβ.
To segment Aβ plaques in the DAPI image, we utilized the cellpose GUI to
identify plaques based on DAPI brightness, plaque size (frequently larger than
cells), and the presence of fibrils and irregular cellular shape. We note that
this methodology may not detect the smallest volume plaques, so our spatial
plaque analysis focuses on the impact of large plaques on neuronal and glial
gene expression.

We utilized the cellpose GUI to manually label DAPI stained plaques in 125
individual ROIs (2000 pixels x 2000 pixels; 0.108 μm per pixel), taken from two
of the 5xFAD and Trem2R47H; 5xFAD samples. Of these, 80% were used for training,
and 20% for validation. A custom cellpose model was trained on the training
data, and error results reported based on the testing set. False positive and
False negatives were manually identified in each test ROI as follows. Contiguous
regions exhibiting the brightness, size and morphology characteristics noted
previously were annotated via the cellpose GUI. These were compared with
predicted plaque locations via the trained cellpose model. Predicted cells
showing significant overlap with annotated regions in the test set were
considered as true positives, while missed regions were labeled as false
negatives, and predicted cells showing no overlap with annotated regions were
considered false positives.

We analyzed false positive and false negative rates, as well as F1 scores
(defined as \(\frac{2* {true\; positives}}{2* {true\; positives}+{false\;
positives}+{false\; negatives}}\)), a metric which balances sensitivity and
specificity, assigning a single score in range [0,1] for model quality. In
testing the model on the hold out annotated plaque data, we identified only 1
false positive and 0 false negatives across 25 FOVs, and 28 total annotated
plaques, resulting in a false positive rate of 0.035, a false negative rate of
0, and an F1 score of 0.89. As additional measures of accuracy, we visually
inspected the identified plaques in comparison with cells and demonstrate that
detected plaques exhibit significantly lower transcript counts than are expected
from cells.

Next, we tested our model on a hold-out 5xFAD sample prepared using the same
methodology as for our analyzed slices, but stained using DAPI, thioflavin S
(canonical Aβ plaque stain) and 6E10 (amyloid beta monoclonal antibody) for
comparison. We annotated plaques using the cellpose GUI cytoplasm model, setting
mean diameter to the same value (205 pixels) as was used in the cellpose plaque
model, utilizing the 6E10 stain results for segmentation. Identified plaques
intersecting the boundary of the ROI were not considered.

We again tested the algorithm on 23 ROIs (2000 pixels x 2000 pixels; 2 ROIs were
removed from a total of 25 test ROIs due to low quality) from this combined
stain slice. This resulted in 73 annotated plaques, of which 76% were detected,
along with one false positive (false positive rate 0.017), and an F1 score of
0.86. The reduced sensitivity was possibly caused by alterations in DAPI
contrast ratios, which affected the algorithms predictive capabilities. Overall,
the protein-stained slices exhibited lower quality, one reason the
protein/antibody stains were not included in the generation of analyzed slices.

In this analysis, no plaques were identified with thioflavin S or 6E10 that did
not also overlap with significant DAPI stains of similar size. In general, DAPI
plaques were identified as slightly larger than ThioS plaques (average 18.6%
greater area, p = 0.018, Wilcoxon rank sum test), although this may be due to
variability in the thresholds used to define plaque boundaries.


FILTERING OF DIFFERENTIALLY EXPRESSED GENES

Due to irregular cellular shapes, cellular processes distal to the soma,
microglia phagocytosis, and possible segmentation errors, overall upregulation
in expression within one cell type, may overlap into another. This pattern was
particularly noticeable in glia, which frequently exhibited expression overlap
in marker genes, as well as neuronal specific genes (such as Slc17a7). As an
example, Gfap was ubiquitously expressed, and differentially expressed in 5xFAD
samples compared with WT in many neuronal cell types. Yet its expression is
limited almost exclusively to astrocytes in previous mouse brain cell atlases
[37, 38].

To filter out these false positive differentially expressed genes, we heavily
annotated all major glia subtypes for gene expression of all 300 genes in the
panel, using both the mousebrain.org [38] and Allen Institute datasets [37]
(Supplementary Table 2). Initial annotation utilized the Allen institute
dataset, requiring a trimmed mean expression greater than zero. This was
supplemented using the mousebrain.org dataset, which is not restricted to the
hippocampus and contains activated microglia and astrocyte cell types.
Differential expression of glial cells was then subset to genes known to be
expressed in these cell types as annotated. Differential expression in neurons
was filtered for expression of glial cell type markers, and disease associated
microglia and astrocyte markers. While we recognize this process may introduce
bias into the differential expression results, raw results exhibited significant
cell type-induced biases complicating analysis of results. Raw differential
expression results for all cell types are available in the supplemental tables.

Additionally, hippocampal differential expression is known to be correlated with
location on the anterior-posterior axis, which varies across samples [46].
Subclustering identified spatially localized subgroups for each cell type,
primarily in the ventral hippocampus. Marker genes for these clusters were
identified, and differentially expressed genes overlapping these subsets were
removed from the analysis. We also compared gene expression with differentially
expressed genes varying on spatial gradients on the anterior-posterior axis in
all brain regions and removed differentially expressed genes previously
identified with such spatial gradients (e.g. we removed any differentially
expressed genes present in the top 600 predictive genes for anterior-posterior
axis location [46]).


COMPUTATION OF CELL DENSITY WITHIN MAJOR REGIONS

To compute the area of identified regions in individual slices, we utilized the
python package alphashape [47], a method for automatically constructing concave
bounding envelopes of point clouds. For each region, we subset to all cells
contained in that region, and utilized alphashape with an alpha of 0.015, and
computed the area of the resulting polygon. The same process was utilized on a
cell type basis to compute neuronal cell density on a cell type level in each
slice. For individual cell types, spatial outliers were removed prior to area
inference via computation of the k-nearest neighbors, and analysis of the 5th
nearest neighbor, using a standard outlier removal technique of eliminating
cells outside the range (median-1.5*IQR, median+1.5*IQR, IQR=interquartile
range), applied to distance to the fifth nearest neighbor. This ensures that
areas are computed only in densely packed cellular regions.

For computing the density of glia in regions proximal and distal to plaques, all
cells in each individual slice within 100 µm (proximal) and between 100 and
500 µm (distal) were used to compute the combined area of all regions distal and
proximal to plaques. This was then used as the normalizing factor to obtain cell
densities proximal and distal to plaques. When investigating density of
individual cell types, we used the region identified using all cells within
50 µm of a single cell of the given subtype, as some cell types were
insufficiently dense for area inference via alpha shape.


STATISTICAL METHODS

Excluding differential expression (described above), statistical tests are
described in the text. We utilized two-sided tests unless otherwise
stated. Differential expression results are presented as adjusted p-values
unless otherwise stated.


RESULTS


IMPACT OF 5XFAD AND TREM2 R47H MUTATIONS ACROSS MAJOR BRAIN CELL TYPES

In this study we investigate regional, plaque proximal, and genotype specific
gene expression changes induced by the Trem2R47H mutation. As this mutation is
not sufficient to induce amyloid plaque pathology in mice, we utilize a
hemizygous 5xFAD/homozygous Trem2R47H mouse model which induces Aβ pathology in
concert with the Trem2R47H mutation, compared with matched 5xFAD (Aβ pathology
only), Trem2R47H, and WT controls (Fig. 1A). By comparing these four genotypes,
we uncover the transcriptomic alterations induced specifically by 5xFAD
transgenes (independent of Trem2R47H mutation), specifically by Trem2R47H
(independent of 5xFAD), and those induced by a combination of 5xFAD and
Trem2R47H mutations.

Fig. 1: MERFISH spatial transcriptomics enables spatial variation analysis of
the transcriptome at the cell type level.

A Dataset overview consisting of 15 samples, from WT, Trem2R47H, 5xFAD and
Trem2R47H; 5xFAD mice. Cell counts in batch 1 are aggregated across two
technical replicates, resulting in approximately double the cell counts of the
other batches. B Integration of cell by gene matrix with RNA spatial location
enables spatial analysis of transcriptomic variation on a regional and genotype
basis. C 300 gene overlay on a single coronal section, at increasing
resolutions. D UMAP displaying 37 annotated cell types after integration across
all samples. E UMAP of cell genotypes. Note the distinct subpopulations specific
to 5xFAD and Trem2R47H; 5xFAD genotypes, particularly in microglia and astrocyte
cell populations. F Hierarchical organization of cell clusters, combined with
raw cell type proportions per genotype.

Full size image

We performed spatial transcriptomic analysis using MERFISH on 19 coronal half
sections from 15 total animals with WT, 5xFAD, Trem2R47H and Trem2R47H; 5xFAD
genotypes at 12 months of age, which age was chosen to match the later timepoint
in our previous studies, which we have extensively characterized [22, 27]. Mice
at this age exhibit an extensive plaque burden throughout the brain, combined
with neuritic damage and glial responses.

After quality control, this dataset resulted in 432,794 cells. Using a 300 gene
panel, we identified 37 major cell types, and transcriptomically and spatially
mapped 5xFAD and Trem2R47H transcriptomic alterations at the single-cell level.
We also identified Aβ plaque locations in the same samples and assessed their
relationship to spatial gene expression (Fig. 1B).

After spatial transcript decoding (Fig. 1C), cells were processed using our
single-cell pipeline (Supplementary Fig. 1A, B), and clusters were identified
based on reference to known cell type markers (Supplementary Fig. 1C), in
conjunction with spatial location (Fig. 1D). Color coding genotype information
on the UMAP shows strong 5xFAD induced cell type composition changes, reaching
significance only in microglia (p = 0.0012, Wilcoxon rank sum test) (Fig. 1E).
Hierarchical clustering identified initial splits between non-neuronal and
neuronal cells, followed by excitatory vs. inhibitory, and spatial (subcortical,
hippocampal, cortical) based splits in excitatory cell types (Fig. 1F).

Visualization of neuronal cell types (Fig. 2A, Supplementary Fig. 2) show strong
spatial localization, commensurate with previous region-based studies and
atlases. Hippocampal excitatory cells define the primary structures of the
hippocampal formation (DG, CA1, CA3), while cortical excitatory neurons divide
into distinct layers across most of the cortex. We identified and visualized
cell type-specific markers for these distinct neuron types (Fig. 2B) to verify
spatial fidelity with raw decoded transcripts.

Fig. 2: Spatial and transcriptomic analysis of coronal brain slices enables
analysis of the spatial distribution of individual genes.

A Spatial position of neuron subpopulations from single coronal sample. B Raw
transcript overlay on PolyT cell body staining, of cell type markers for a
subset of the neuron subpopulations in A. C Annotated spatial regions for a
single coronal sample. Annotation performed based on transcriptomic cellular
locations, combined with the Allen mouse brain reference atlas. D Raw transcript
overlays of Tmem119 (homeostatic microglia), Itgax (disease associated
microglia), and Trem2 on top of DAPI nuclei staining (blue). Point brightness
indicates pixels where multiple individual transcripts were aggregated. E Violin
plots of normalized expression of the genes indicated in D, divided by genotype
and aggregated across samples. Asterisks indicate statistical significance
(p < 0.05, linear mixed effects model). F Regional mean normalized expression in
microglia aggregated within genotypes.

Full size image

Next, we segmented major brain regions, subdividing the cortex into three
subregions: the neocortex (somatosensory, visual, parietal, retrosplenial, and
auditory cortices), the limbic cortex (perirhinal, ectorhinal, entorhinal, and
piriform cortices), and the cortical amygdala, and identify major structures in
hippocampal and subcortical regions. This resulted in 10 identified major brain
regions (Fig. 2C).

We visualized raw transcript counts of Tmem119 and Itgax to confirm microglia
activation in the 5xFAD and Trem2R47H; 5xFAD mice. Tmem119 is a homeostatic
microglia marker, while Itgax is a marker for disease associated microglia
(DAM), a distinctive microglia subset whose activation is associated with
neuroinflammatory responses, including response to Aβ plaque pathology. As
expected, 5xFAD and Trem2R47H; 5xFAD mice show Itgax expression upregulation,
indicating increased microglial activation (Fig. 2D–E, Itgax: p < 0.02, Tmem119:
p < 10−10, linear mixed effects model). Microglia transition to a fully
activated state via a two-stage Trem2 dependent pathway, highlighting the
importance of this gene in AD progression [48]. We note that Trem2 expression is
significantly increased in the microglia of both 5xFAD and Trem2R47H; 5xFAD mice
(Fig. 2E, 5xFAD: adjusted p = 2.6 × 10−3, fold change = 1.88, Trem2R47H; 5xFAD:
adjusted p = 7.1 × 10−6, fold change = 1.89. Linear mixed effects model). We
also note that Itgax and Trem2 expression is consistently upregulated in 5xFAD
and Trem2R47H; 5xFAD mice across most regions (Fig. 2F).

Finally, we analyzed neuronal density for each cell type by computing the total
number of detected neurons divided by the estimated volume of the associated
regions. No statistically significant trend was identified, however this may be
due to our relatively small number of sample sections per genotype and the
section variance across samples.

Overall, MERFISH spatial transcriptomics enables detection of high-level cell
type clusters, visually identifiable and quantifiable transcriptomic differences
in microglia and regional annotation and assignment of individual cells to
specific coarse-grained spatial regions.


GLIAL AND NEURONAL TRANSCRIPTOMES ARE AFFECTED BY NEARBY PLAQUES

Spatial transcriptomics can reveal local effects of pathology, such as Aβ
plaques, on the regulation of gene expression in nearby cells. By co-staining
coronal brain slices with both DAPI and thioflavin S (a canonical stain for Aβ
plaques), we observed that DAPI brightly labels Aβ plaques in addition to nuclei
[49] (Fig. 3A). We therefore applied DAPI staining to MERFISH prepared coronal
slices and a machine learning approach to automatically detect and segment
plaques in each of the MERFISH samples.

Fig. 3: Machine learning enables accurate identification of plaque locations
across brain slices.

A DAPI (nuclei staining), ThioS (plaque staining) and overlay indicate that DAPI
stains both nuclei and plaques. B Manual annotation of Aβ plaques
(differentiated from cells by size, brightness, and morphology) is used as the
basis for a machine learning model to detect plaque locations. C Detected
plaques (yellow) in a single 5xFAD sample. Zooming in (right panel) we see that
the machine learning model identifies the plaque, but not the cells surrounding
it (manually circled, green). D Plaques exhibit significantly lower transcript
density than cells (p < 0.0001, t-test), and significantly higher volume
(p < 0.0001, t-test).

Full size image

DAPI stained plaques are visually distinguishable from nuclei by their large
size, greater brightness, and fibrous morphology and lack of circular cell soma
shape (Fig. 3B, C). These features enable manual annotation of plaques in
individual fields of view. We trained a modified cellpose model [34] to detect
large plaques (mean diameter 22.4 µm), but not cells (mean diameter 8.5 µm)
(Fig. 3B, Supplementary Fig. 3A, B).

We analyzed each 5xFAD and Trem2R47H; 5xFAD section using this model
(Supplementary Fig. 3C, D) and verified that (1) the model does not detect cells
(Fig. 3B), (2) the predicted plaques are morphologically distinct from cells
(Fig. 3C), and (3) the predicted plaques have significantly lower transcript
density when compared to cells (Fig. 3D, t-test, p < 0.0001), as well as
greater volume (t-test, p < .0001). Across all 5xFAD and Trem2R47H; 5xFAD
samples, we identified a total of 5616 plaques (per sample: 5xFAD- 659.2 ±
160.1, Trem2R47H; 5xFAD- 464 ± 146.9, mean ± s.e.).

Across brain regions, we found the closest cell to each identified plaque was
most frequently microglial (62.6% of plaques in 5xFAD and 60.0% in Trem2R47H;
5xFAD) (Fig. 4A). Additionally, microglia density in the region within 100 µm of
a plaque (proximal) was significantly higher than in the 100–500 µm
region(distal) (5xFAD, proximal density = 17.6 ± .935 × 10−5, distal density =
7.81 ± 0.665 × 10−5, p = 7.55 × 10−4; Trem2R47H; 5xFAD, proximal density = 17.6
± 0.790 × 10−5, distal density = 6.87 ± 2.15 × 10−5, 1.31 × 10−3, mean ± s.e,
plaques/µm2), while no genotype difference was detected for density either
proximal or distal to plaques (p > 0.19). Astrocytes were the second most common
cell type identified near plaques (7.9% of plaques in 5xFAD and 10.1% in
Trem2R47H; 5xFAD) (Fig. 4A), however, overall astrocyte density showed no
differences in density between proximal or distal areas in either genotype
(p > 0.16). Thus, the typical microenvironment around plaques includes
microglia, with astrocytes and other cell types at greater distances from the
plaque (Fig. 4B) [24, 50]. We then analyzed cell type proportions in annuli
around individual plaques measured at 25 µm intervals. The smallest 25 µm circle
around each plaque center was populated almost exclusively by microglia, with
other cell types becoming more prevalent with increasing distance to plaque
(Fig. 4C).

Fig. 4: Aβ plaque proximity causes transcriptomic dysregulation in both glia and
neuronal cell types.

A Example plaque (arrow) with associated annotated cell types. Note the
congregation of microglia around the plaque. B Proportion of cell types
identified as closest to plaques. For each plaque, the closest cell was
identified, and the proportion of resulting cell types was computed. C Cell type
proportions within annuli at specific distances from plaque centers. The top row
indicates raw cell proportions, while the bottom row shows cell type proportions
after normalization by the total number of cells in that group. D Aβ plaque
density by region. Statistical comparison (p < 0.01, linear mixed effects model)
identifies three regions with significantly lower plaque density in Trem2R47H;
5xFAD animals. E Differential expression results for microglia and astrocytes
using distance to plaque as the continuous dependent variable. Cells were
selected such that all tested cells were within 100 µm of the center of a
plaque. Cells aggregated across 5xFAD and Trem2R47H; 5xFAD samples. Genes
filtered by expression in the associated cell type as identified in previous
studies. No other cell types exhibited more than one differentially expressed
gene in this test. Results indicate expected expression changes per µm distance
increase from closest plaque center. F Differential expression results testing
cells within 100 µm of the center of a plaque against those 100–500 µm from the
center of a plaque. The cell types with the largest number of DE genes among
glia and neurons are visualized here. Genes filtered by expression in the
associated cell type as identified in previous studies. Red points indicate
genes exceeding both adjusted p-value and log fold change thresholds, green
points only exceed log fold change thresholds, blue points only exceed adjusted
p threshold, and gray points exceed no threshold.

Full size image

We assessed whether plaques appear proximal to neurons. The distance from a
plaque to the closest neuron was significantly larger than the distance from a
neuron to its closest neuronal neighbor (5xFAD: minimal plaque to neuron
distance 56.4 ± 10.7 µm, minimal neuron to neuron distance 21.6 ± 1.24 µm,
p = 0.012; Trem2R47H; 5xFAD: minimal plaque to neuron distance 48.7 ± 2.53 µm,
minimal neuron to neuron distance 21.0 ± 0.733 µm, p = 1.47 × 10−4, mean ± s.e.,
plaques in corpus callosum excluded from analysis due to lack of nearby neurons,
t-test). We examined the typical distance of each neuronal cell type to the
nearest plaque. This analysis showed that subiculum, layer 5, and layer 6
excitatory neurons have the lowest median distance to plaques among identified
cell types (Supplementary Fig. 3E). However, none of the top 5 neuron types
(ranked by median distance to plaque, excluding subiculum excitatory and
SST-Chodl cells due to low cell numbers), exhibited significant density
variation between plaque proximal (<100 µm) and distal (100–500 µm) regions.
This implies that neuronal plaque proximity is driven primarily by plaque
density in the associated regions. Additionally, plaques on average form in
regions nearly twice as far from the nearest neuron as the typical distance
between neurons, but the average neuronal density does not appear to be
decreased in plaque proximal vs. plaque distal regions, implying a variation in
plaque to neuron distance at the microscale (<100 µm), but not at larger scales
(<500 µm).

The highest plaque density occurred in the corpus callosum (CC) (5xFAD average
5.61 × 10−5, Trem2R47H; 5xFAD average 6.23 × 10−5 plaques/µm2) and hippocampal
areas (5xFAD average 2.89 × 10−5, Trem2R47H; 5xFAD average 2.04 × 10−5
plaques/µm2, averaged across CA1, CA3, and DG), followed by cortex (5xFAD
average 3.92 × 10−5, Trem2R47H; 5xFAD average 2.08 × 10−5 plaques/µm2, averaged
across neocortex, limbic cortex, and cortical amygdala), with the lowest
densities in the subcortical regions (5xFAD average 2.71 × 10−5, Trem2R47H;
5xFAD average 0.323 × 10−5 plaques/µm2, averaged across midbrain, thalamus and
hypothalamus) (Fig. 4D). Mice with the 5xFAD genotype had higher plaque density
compared with Trem2R47H; 5xFAD mice in the midbrain, thalamus, and neocortex
(p < 0.05, linear mixed effects model), but not the CC. This distribution is
consistent with the pattern of median minimum distance to plaques (Supplementary
Fig. 3E), with roughly all cell types showing larger distance to plaques in
Trem2R47H; 5xFAD samples. High plaque density regions such as the subiculum and
lower cortical layers contained neurons with the lowest median distance to the
nearest plaque. Trem2R47H; 5xFAD animals exhibited larger plaque sizes than
5xFAD animals (1108 µm3 vs. 984 µm3, p = 0.025, Wilcoxon rank sum test), though
this appears to be gender and pathology dependent, as male animals showed the
reverse effect (741.96 µm3 vs. 799.60 µm3, p = 0.0046) as well as lower
pathology levels (Supplementary Fig. 3C, D).

We next tested whether cells within 100 µm of the nearest Aβ plaque have altered
patterns of gene expression (Fig. 4E). Due to the relatively low cell abundance
proximal to plaques, we aggregated 5xFAD and Trem2R47H; 5xFAD samples, and
separated individual cells by cluster. We analyzed plaque proximity based
differential expression with two techniques. First, we identified cells within
100 µm of a plaque center. Using DESeq2 and treating cells as independent
samples, we identified genes whose expression correlated with proximity to the
nearest plaque (Fig. 4E, Supplementary Table 3). Continuous effects were
identified primarily in microglia and astrocytes, with microglia showing an
upregulation of typical DAM associated genes (e.g. Csf1, Apoe, Cst7), and a
downregulation of P2ry12, a homeostatic microglia associated gene [51].
Similarly, C4b, Clu, and Gfap, markers of a previously known disease associated
astrocyte (DAA) phenotype were also upregulated near plaques [52].

To validate these findings and to account for variability across biological
replicates, we additionally performed a pseudobulk analysis of differential
expression between plaque-proximal (within 100 µm of the closest plaque) and
plaque-distal (100–500 µm to closest plaque). We applied a linear mixed effects
model to pseudobulk expression for each cell type in each sample, accounting for
batch as a random effect (Fig. 4F, Supplementary Table 4). Additionally, we
filtered genes based on their known expression in each cell type from previous
single-cell atlases [37, 38], to avoid spurious identification of differentially
expressed genes due to technical (errors in segmentation) or biological
(phagocytosis, overlapping cellular processes) effects [53].

Pseudobulk analysis was generally consistent with the DESeq2 results and
identified both glial and neuronal changes (Fig. 4F). Microglia and astrocytes
exhibited typical disease associated profiles in cells located proximal
(<100 µm) to plaque centers. However, Nnat expression in astrocytes and
oligodendrocytes, and Mmp14 expression in astrocytes decreased near plaques.
This result contrasts with previous findings in humans and other mouse models
showing upregulation of Mmp14 in reactive astrocytes in AD [54].

The pseudobulk analysis also revealed notable changes in gene expression
affecting neurons proximal to plaques (Fig. 4F, bottom row). L6b neurons showed
lower Ngf expression near plaques, a gene therapy target in AD [55]. Nr2f2,
upregulated near plaques, is known to be dysregulated by AD associated single
nucleotide polymorphisms in the APOE enhancer [56]. Cnr1 and Htr1a, also
upregulated near plaques, are linked to regulation of the serotonergic system,
which is known to affect memory in the context of AD [57]. L2 intratelencephalic
(IT) neurons near plaques showed downregulation of Dkk3 (a WNT signaling
modulator whose presence reduces Aβ pathology in mouse models [58]) and of the
potassium ion channel subunit Kcnd2 [59] near plaques. L5 NP cells show Grm1
upregulation and Chrna7 downregulation near plaques. Parvalbumin-expressing
inhibitory cells shows Grin2a, Zbtb20, and Plagl1 downregulation near plaques.
Excitatory neurons in the cortical amygdala exhibited downregulation of Ntf3
(associated with nervous system maintenance [60]), Nptx1 (associated with
synapse remodeling, but typically upregulated in previous studies of cortical
neurons near plaques [61]), and Camk2g (implicated in synaptic plasticity [62]).
Because there were few plaques in subcortical regions, we did not test
plaque-associated differential expression for neuronal cell types in this
region.


MICROGLIA AND ASTROCYTES EXHIBIT DISTINCT CELL TYPE-SPECIFIC SPATIAL PATTERNS OF
ACTIVATION ASSOCIATED WITH 5XFAD MUTATION

We next directly analyzed spatial and transcriptomic variation of glia between
genotypes. We made four pairwise comparisons (5xFAD vs. WT, Trem2R47H; 5xFAD vs.
Trem2R47H, Trem2R47H vs. WT, Trem2R47H; 5xFAD vs. 5xFAD), to identify 5xFAD and
Trem2R47H dependent variations, which we then compared with differential
expression between Trem2R47H; 5xFAD and WT (Supplementary Table 5).

We identified 19 differentially expressed genes in microglia and 8 in astrocytes
across all four pairwise comparisons. By contrast, we found 1–2 differentially
expressed genes in oligodendrocyte (OGC) and oligodendrocyte precursor cells
(OPC) cell populations (Fig. 5A), and none in the other non-neuronal cell types.
Microglia and astrocytes primarily exhibited 5xFAD dependent changes (similar
differential expression results for both 5xFAD vs. WT, and Trem2R47H; 5xFAD vs.
Trem2R47H), replicating the DAM/DAA gene upregulation and homeostatic gene
downregulation identified in the plaque proximity analysis. These were widely
replicated in differential comparison of Trem2R47H; 5xFAD and WT. Interestingly,
neither Itgax nor Cd74 were identified as differentially expressed in plaque
proximity analysis of microglia, whereas they were upregulated 9.58 and
15.7-fold in 5xFAD compared with WT animals, and 19.7 and 26.0-fold in
Trem2R47H; 5xFAD compared with Trem2R47H animals. The Trem2 gene itself showed a
small reduction in expression dependent on the Trem2R47H mutation, which we
hypothesize may be due to reduced binding efficiency of gene probes overlapping
the mutated region, as the effect was not seen in our previous study [22].
Differential expression also shows a small but significant Trem2R47H specific
upregulation in homeostatic microglia genes, including Tmem119 (fold change =
1.13, adjusted p = 0.019, Trem2R47H; 5xFAD vs. 5xFAD), and P2ry12 (fold change =
1.27, adjusted p = 4.60 × 10−4, Trem2R47H; 5xFAD vs. 5xFAD). The consistent
variation in both Trem2R47H vs. WT and Trem2R47H; 5xFAD vs. 5xFAD comparisons
indicates this may be a plaque independent effect and corroborates the overall
lower plaque burden in Trem2R47H; 5xFAD samples.

Fig. 5: Microglia and astrocytes exhibit 5xFAD induced transcriptome
alterations.

A Pairwise differential expression between genotypes among glia populations.
Five pairwise comparisons are indicated (5xFAD vs. WT, Trem2R47H; 5xFAD vs.
Trem2R47H, Trem2R47H vs. WT, Trem2R47H; 5xFAD vs. 5xFAD, Trem2R47H; 5xFAD vs.
WT). Heatmaps display log fold change for each comparison, with genes not
exceeding significance set to 0. Heatmaps are thresholded to the range (−1, 1).
Genes not exhibiting significant expression in the associated cell type
according to the Allen or mousebrain references were removed. Cell types
exhibiting no differentially expressed genes not shown. B (1) Subclustering
results for microglia. Clusters with transcriptomes influenced by spatial
colocalization with other cell types removed. (2) Diffusion pseudotime results,
indicating a non-bifurcating differentiation trajectory. (3) Genotype
proportions for each subcluster. Clusters C1-4 are found primarily in 5xFAD and
Trem2R47H; 5xFAD mice, with C6-7 localized to WT and Trem2R47H animals.
Asterisked clusters pass threshold for overabundance. Results indicate the
pseudotime trajectory (1) describes a genotype specific transition. (4)
Proportions of DAM and homeostatic microglia within annotated regions.
Significant variations in distribution include decreased DAM proportions in
cortex, DG, CA3 and hypothalamus. (5) Upregulated genes in each subcluster
divide into homeostatic and DAM associated genes. C Astrocyte subclustering
analysis. (1) Subclustering results, unbiased (by genotype proportion) clusters
combined and relabeled as C1. (2) Pseudotime trajectories indicate no clear
differentiation pattern. (3) C2-3 exhibit 5xFAD and Trem2R47H; 5xFAD
upregulation, with C4-5 upregulated in WT and Trem2R47H mice. (4) Significant
spatial variation indicates C5 and C4 are differentiated by spatial location
(subcortical vs. cortex/hippocampus), while C2 also appears upregulated in
Cortex and Hippocampus, and C3 is distributed evenly across regions. (5) C2-3
exhibit upregulation of C4b and Gfap, part of the disease associated astrocyte
(DAA) phenotype, while the spatially variable C4 and C5 differentiate by Cspg5
and Camk2g expression. D (1) spatial distribution of DAM and homeostatic
microglia overlaid on a 5xFAD sample. (2) DAM proportion of total microglia in
each region. Error bars indicate standard errors. Asterisks indicate regional
differences between 5xFAD and Trem2R47H; 5xFAD mice (p < 0.05, linear mixed
effects model). Highest concentrations of DAM in CC, midbrain, and thalamus. (3)
DAM proportions of microglia divided by cortical layer. No statistically
significant change detected between 5xFAD and Trem2R47H; 5xFAD mice, but
statistically significant increases in DAM proportion in lower cortical layers.
E (1) spatial distribution of DAA and homeostatic astrocytes overlaid on a 5xFAD
sample. (2) DAA proportion of total microglia in each region. Error bars
indicate standard errors. No statistically significant variations identified
between genotypes. Highest concentrations of DAA in CC and surrounding regions.
(3) DAA proportions of microglia divided by cortical layer. No statistically
significant change detected between 5xFAD and Trem2R47H; 5xFAD mice, but
statistically significant increases in DAA proportion in lower cortical layers.

Full size image

To explore the effects of AD risk genes in specific glial subtypes, we
subclustered the microglia and astrocyte subpopulations. We identified several
small clusters of microglia that appear to express neuronal or other glial
markers, and we confirmed that these cells are located near cells expressing
these markers. We removed these cells from this portion of the analysis. After
removal, subclustering identifies 7 microglia clusters (Fig. 5B1). Pseudotime
analysis identified a single linear trajectory across all microglial cell types
(Fig. 5B2). We next examined the genotype proportions of these clusters. After
normalizing by the number of cells per sample, we averaged across samples of the
same genotype, and computed cluster proportions. This identifies a clear 5xFAD
dependent bias, with two clusters (labeled homeostatic) exhibiting > 80%
proportion coming from non 5xFAD (i.e. WT and Trem2R47H) samples. The remaining
five clusters corresponded to disease associated microglia (DAM) enriched in
5xFAD and Trem2R47H; 5xFAD mice (Fig. 5B3).

We aggregated homeostatic and DAM subgroupings and identified regional spatial
biases (Fig. 5B4). DAMs were enriched in hippocampal area CA1. They were also
enriched in thalamus, and midbrain, despite the relative lack of plaque density
in these regions compared to the CA1 and CC (Fig. 4D). Finally, we identified
markers for the individual microglia subpopulations, and plot normalized
expression (Fig. 5B5).

We focused on the analysis of the genes differentially associated with
late-stage DAMs as several genes exclusive to late-stage DAM (DAM II) were
included (Itgax, Cst7, Csf1, Ccl6), as well as genes present across both stages
(Apoe). Except for Ccl6, all of these genes are differentially expressed in
5xFAD and Trem2R47H; 5xFAD, with primary expression of DAM2 genes in C3-5 (later
pseudotime). Apoe is evenly distributed across C2-5, reflecting its
overexpression across the DAM developmental timeline (Fig. 5B5) [63]. While we
do not have explicit genes that encode DAM I compared with DAM II in the gene
panel, clusters C1-2 likely contain DAM I microglia, based on the
pseudodevelopmental timeline.

Subclustering the astrocyte subpopulations, we aggregated clusters not
exhibiting genotype specific bias (see methods for thresholds) into a single
cluster (C1), retaining the genotype biased clusters (Fig. 5C1). Pseudotime
trajectory analysis (Fig. 5C2) did not yield a distinctive pattern, however
after analysis of genotype bias (identifying C1 as unbiased, C2/C3 as DAA, and
C4/C5 as upregulated in WT/Trem2R47H samples, Fig. 5C3), we note that C5 and C4
exhibited distinct spatial distributions, with C4 appearing exclusively in
cortex and hippocampus, and C5 appearing in subcortical regions (Fig. 5C4). The
DAA exhibited a similar regional specificity, with C2 primarily restricted to
cortex and hippocampus. Cluster markers are identified and plotted (Fig. 5C5).

We next examined the spatial distribution of DAM and DAA cells by region.
Disease associated microglia were enriched in the CC, subiculum and subcortical
regions (Fig. 5D1). Computing the proportion of microglia identified as DAM by
region (Fig. 5D2) showed similar proportions of DAMs between 5xFAD and
Trem2R47H; 5xFAD samples by region, except in the DG, thalamus, midbrain, and
hypothalamus. This corresponds with the plaque density bias in 5xFAD samples
(Fig. 4D). In the cortex, we saw a significant increase in DAMs in the lower
cortical layers (L5/L6) compared with the upper cortical layers (L2/L3) (p <
0.0001, linear mixed effects model, Fig. 5D3).

Disease associated astrocytes were concentrated in the CC and surrounding areas
(Fig. 5E1-2). Virtually no disease associated astrocytes were present in upper
cortical layers, but this population was significantly upregulated in deeper
cortical layers (Fig. 5E3). We did not find significant genotype specific
effects in other glial cells.

To further analyze the spatial variation of gene expression, we performed direct
pseudobulk differential expression analysis of microglia, astrocytes,
oligodendrocytes and oligo-precursors across the 10 identified major brain
regions (Supplementary Fig. 4, Supplementary Table 6). We compared each region
with the average across the remaining 9 regions. We also computed regional cell
density and cell proportion for each of these cell types.

Analysis of microglia (Supplementary Fig. 4A) showed that ~60% of spatially
variable genes were also differentially expressed across genotypes. For example,
the canonical late-stage DAM markers Cst7 and Itgax were significantly
upregulated in the corpus callosum. A small number of other genes (Ctss, C1qa,
Zbtb20, Ly9, Tmem119) had spatially variable patterns of expression that were
consistent across WT and Trem2R47H mice and dysregulated in 5xFAD and Trem2R47H;
5xFAD mice. Microglia cell populations also showed drastic increases in both
cell proportion and density across all brain regions.

Astrocytes (Supplementary Fig. 4B) exhibited large numbers of spatially variable
genes with consistent patterns of expression across all genotypes (e.g. Erbb4,
Nnat, Grin3a, Mmp14, Id4, Pax6, etc). We also found spatial variation in several
disease associated genes (Aqp4, Gfap). These spatial variations were primarily
observed between cortical and subcortical (thalamus, midbrain, hypothalamus)
regions. However, astrocytes exhibited little genotype specific cell proportion
or density variations between regions.

Oligodendrocytes exhibited two separate gene groupings (Supplementary Fig. 4C).
One set (Snca, Dlg4, Nnat, Robo1, S100b, Ptgds) showed spatially variable
expression across multiple regions, particularly between cortical/hippocampal
and subcortical regions. The other set of genes (Adam10, Psen1, Olig1, etc) is
primarily upregulated in CC and downregulated in amygdala, with very little
variation in other regions. This pattern is not 5xFAD or Trem2R47H dependent and
was observed even in WT oligodendrocytes cells. This second pattern is not
replicated in oligodendrocyte precursor cells, though a significant cortex vs.
subcortical divide is present in OPCs (Supplementary Fig. 4D). Neither cell type
exhibits significant genotype dependent cell proportion changes within regions.

Overall, our data show that spatial variation in microglia and astrocyte gene
expression is more affected by 5xFAD than by Trem2R47H. Both disease-associated
microglia and astrocytes exhibit specific spatial distributions. DAMs were
distributed across the coronal section, but concentrated in the CC and
subcortical regions, and DAA were biased almost exclusively to the CC and
surrounding regions. Regional transcriptional variations were primarily impacted
in 5xFAD for microglia and astrocytes, and both 5xFAD and Trem2R47H mutations
were independent of regional variations in oligodendrocytes and oligodendrocyte
precursors.


NEURONS EXHIBIT COMPLEX TRANSCRIPTOMIC IMPACTS OF 5XFAD AND TREM2R47H MUTATIONS

We performed differential expression analysis for each of the four comparisons
(5xFAD vs. WT, Trem2R47H; 5xFAD vs. Trem2R47H, Trem2R47H vs. WT, Trem2R47H;
5xFAD vs. 5xFAD), and compared with Trem2R47H; 5xFAD vs. WT differentially
expressed genes, followed by subclustering analysis for each of the neuronal
cell types (Supplementary Table 5). Analysis of cortical neurons identifies
differentially expressed genes for all these comparisons in each cell type
(Fig. 6A–C), as well as genotype biased subclusters for most neuron cell types
(Fig. 6D, E, Supplementary Fig. 5). We first considered genes consistently
identified as differentially expressed across multiple cortical neuronal cell
types.

Fig. 6: Cortical neurons exhibit consistent Trem2 associated transcriptomic
variations and spatially localized genotype biased subclusters.

A Pseudobulk, linear mixed effects model differential expression results for
cortical IT neurons. Heatmaps indicate log fold changes. Fold changes for genes
not exhibiting significance set to 0 (white). B Differential expression results
for other cortical excitatory cell types. C Log fold expression changes for each
comparison for genes identified as consistently differentially expressed across
multiple cell types. D Subclustering of L2 IT neurons (UMAP, top left)
identifies a single subcluster overrepresented in WT and Trem2R47H samples (top
right, asterisked). This cluster is homogeneously distributed along layer 2 with
bias for the neocortex (bottom left), and exhibits overrepresentation of Grp,
Nos1, Nptx1, and Ptk2b. E Subclustering of L5 NP neurons (UMAP, top left)
identifies a single subcluster overrepresented in WT and Trem2R47H samples (top
right, asterisked). This cluster is spatially localized to the retrosplenial
cortex near the subiculum, an area of high plaque density. This cluster exhibits
overrepresentation of Ptpru, Cplx1, Sulf2, and Deptor.

Full size image

Cdh12, associated with calcium ion binding [64], was differentially expressed in
5 out of 9 cell types. Both L3 IT and L5 IT neurons exhibit upregulation of
Cdh12 in Trem2R47H; 5xFAD over 5xFAD genotypes. Ntrk2, which encodes TrkB, a
high affinity receptor for BDNF [65], is upregulated in Trem2R47H; 5xFAD vs.
5xFAD and Trem2R47H vs. WT comparisons in L2 IT, L3 IT, L6 IT and L6 CT
excitatory cell types. On the other hand, Bdnf itself, expected to decrease in
the 5xFAD context, was identified as significantly decreased only in L2 IT and
L6b neurons. Fos, a molecular marker of neuron activity [66], was consistently
identified as differentially expressed across 6 of the 9 cell types, for at
least one comparison. In each case this gene was downregulated, implying
downregulation of Fos induced by both 5xFAD and Trem2R47H mutations.

All other genes were differentially expressed in at most 3 cortical excitatory
cell types. Of these, the most interesting are Wfs1, recently implicated in Tau
clearance in AD [67], which is downregulated in Trem2R47H; 5xFAD compared with
5xFAD, and Grm2, a glutamate receptor downregulated in Trem2R47H; 5xFAD compared
with 5xFAD in L3 IT and L6 IT neurons.

We next examined log fold changes for the previously mentioned genes without
applying statistical thresholds, to identify possible patterns across cell
types obfuscated by our choice of threshold (Fig. 6C). Cdh12 showed no
consistent patterns in the 5xFAD comparisons but was consistently upregulated by
the Trem2R47H mutation (Trem2R47H vs. WT: 0.591 ± 0.305, p = 3.79 × 10−3;
Trem2R47H; 5xFAD vs. 5xFAD: 0.305 ± 0.197, 8.49 × 10−4; mean ± sd, computed
average across cell types, t-test). Ntrk2 exhibited a similar pattern (Trem2R47H
vs. WT: 0.706 +/− 0.366; Trem2R47H; 5xFAD vs. 5xFAD: 0.554 ± 0.330) and also
showed a significant downregulation in the Trem2R47H; 5xFAD vs. Trem2R47H
comparison (−0.207 ± 0.133, p = 8.25 × 10−4). Bdnf did show a small decrease
induced by 5xFAD (5xFAD vs. WT: −0.179 ± 0.132, p = 2.04 × 10−3; Trem2R47H;
5xFAD vs. Trem2R47H: −0.207 ± 0.133, p = 4.56 × 10−4), and interestingly, a
consistent upregulation induced by Trem2R47H (Trem2R47H vs. WT: 0.581 ± 0.360,
p = 6.50 × 10−4; Trem2R47H; 5xFAD vs. 5xFAD: 0.273 ± 0.374, p = 4.65 × 10−2),
except in L5 PT and L6b neurons. Both Wfs1 (Trem2R47H vs. WT: −0.199 ± 0.209,
p = 1.47 × 10−2; Trem2R47H; 5xFAD vs. 5xFAD: −0.448 ± 0.140, p = 3.18 × 10−6)
and Grm2 (Trem2R47H vs. WT: −0.580 ± 0.481, p = 6.76 × 10−3; Trem2R47H; 5xFAD
vs. 5xFAD: −0.234 ± 0.194, p = 6.91 × 10−3) were consistently downregulated by
the Trem2R47H mutation. Fos exhibits negative log fold changes in every cell
type and comparison (5xFAD vs. WT: −0.778 ± 0.282, 3.47 × 10−5; Trem2R47H; 5xFAD
vs. Trem2R47H: −0.522 ± 0.227, p = 7.96 × 10−5; Trem2R47H vs. WT: −0.850 ±
0.370, p = 1.25 × 10−4; Trem2R47H; 5xFAD vs. 5xFAD: −0.552 ± 0.228,
p = 8.57 × 10−5), indicating highly consistent activity downregulation induced
by both 5xFAD and Trem2R47H mutations.

Subclustering cortical neurons identified genotype-specific subpopulations in 7
cortical excitatory cell types (Fig. 6C–E, Supplementary Fig. 5). In all but one
cell type (L3 IT), this represents a WT (or WT/Trem2R47H) enriched, and thus
5xFAD/Trem2R47H; 5xFAD reduced subpopulation. For most IT cell populations,
these genotype specific subtypes did not exhibit significant spatial
localization. However, 5xFAD/Trem2R47H; 5xFAD reduced subtypes in L2 IT are
spatially localized to the retrosplenial and visual cortices, and
5xFAD/Trem2R47H; 5xFAD reduced subtypes in L5 NP are spatially localized in the
retrosplenial cortex near the subiculum, a plaque dense environment (Fig. 6E,
Supplementary Fig. 5). This latter subtype upregulated Sulf2 (antibody staining
has shown this is reduced in AD [68]) and Cplx1 (regulates synaptic transmission
by preventing neurotransmitter release prior to action potential [69]) as top
differentially expressed genes.

Among subcortical neurons, thalamic excitatory neurons exhibited the largest
number of differentially expressed genes (Fig. 7A). Thalamic excitatory neurons
exhibit 5xFAD induced upregulation of Grin2c, Epha10, Ptpru, and Crtac1, with
downregulation of Syp, Bdnf, Negr1, and Gsto1, each of which has been linked to
AD [70,71,72,73,74,75,76]. On the other hand, Ntsr1, Kcnh7, Map4k3, and Col11a1
are upregulated in Trem2R47H; 5xFAD over 5xFAD. No consistent effects can be
attributed to either the 5xFAD or Trem2R47H in subcortical non-thalamic
inhibitory and excitatory neurons.

Fig. 7: CA3 excitatory neurons exhibit significant 5xFAD induced transcriptional
differences.

Differential expression analysis of subcortical neurons A, Hippocampal
excitatory neurons B, and Inhibitory interneurons C. Due to slice variation on
the anterior to posterior axis, there are spatial biases in some cell types.
Differential gene expression associated with possible spatial biases (as a
confounding factor) are removed. Excitatory thalamic and CA1 excitatory neurons
exhibit significant consistent variation on comparisons 1 and 2 (5xFAD vs. WT
and Trem2R47H; 5xFAD vs. Trem2R47H, A, B). D Subclustering of CA3 excitatory
neurons identifies a 5xFAD upregulated population (C2, asterisked) as well as
two WT and Trem2R47H upregulated populations (two additional subclusters were
removed due to sample location bias).

Full size image

Hippocampal CA1 excitatory neurons (Fig. 7B) exhibited upregulation of the
expression of Ntrk2 and Mapk1, associated with the MAPK signaling pathway, in
Trem2R47H; 5xFAD compared with 5xFAD animals. CA3 excitatory neurons showed
several differentially expressed genes (Rph3a, Itga7, Hs3st1), but almost
exclusively in the 5xFAD vs. WT comparison, though Ntsr1 was upregulated in both
Trem2R47H; 5xFAD vs. 5xFAD, and Trem2R47H vs. WT comparisons. The dentate gyrus
showed upregulation of Dkk3 and downregulation of Adgra1 in both 5xFAD dependent
comparisons.

Inhibitory cell types (Fig. 7C) consistently exhibited upregulation of Epha10 in
5xFAD compared with WT, and downregulation in Trem2R47H; 5xFAD compared with
5xFAD. Few other genes (Fos, Hrh3) were consistently differentially expressed
between genotypes.

Differentially expressed genes between Trem2R47H; 5xFAD and WT genotypes
(Figs. 6, 7, Supplementary Table 5) matched with the other four comparisons, in
that up/downregulation matched that expected by the previous comparisons (e.g.
differential expression follows trends in 5xFAD and Trem2R47H induced
alterations). However, the sensitivity is likely lower, as due to loss of one WT
sample, only two imaging batches contained both WT and Trem2R47H; 5xFAD
genotypes. Critically, the results indicating upregulation of Ntrk2 are
replicated in this comparison.

Subclustering subcortical and hippocampal neurons (Supplementary Fig. 6) reveals
greater genotype proportion heterogeneity than in cortical excitatory neurons.
In contrast with cortical excitatory neurons, hippocampal and thalamic
excitatory neurons clustered into large numbers of variable genotype proportion
subclusters. Thalamic excitatory neurons subcluster into three genotype enriched
sets, including a 5xFAD/Trem2R47H; 5xFAD enriched subtype a Trem2R47H/Trem2R47H;
5xFAD enriched subtype and a WT/Trem2R47H enriched subtype. CA1 excitatory
neurons identified 6 genotype enriched subclusters, though two of them are
spatially localized in the ventral CA1, which is not included in some samples.
These include two 5xFAD/Trem2R47H; 5xFAD enriched subtypes and two WT/Trem2R47H
enriched subtypes. CA3 excitatory neurons subclustered into 5 genotype biased
subclusters, including several localized to the ventral hippocampus (Fig. 7D,
ventral hippocampus clusters not shown). One subcluster, labeled C2, present
primarily in 5xFAD and Trem2R47H; 5xFAD samples, is spatially positioned in the
intersection of the CA3 and dentate gyrus. This cluster upregulated Rph3a and
Dkk3 (Supplementary Fig. 6C).

Overall, neuronal populations exhibit transcriptional alterations associated
with both 5xFAD and Trem2R47H mutations. In cortical excitatory neurons, these
changes are frequently replicated across cell types. Thalamic excitatory
neurons, uniquely among subcortical populations, exhibit significant 5xFAD and
Trem2R47H induced transcriptomic alterations. In the hippocampus, the CA1 shows
the most transcriptional alteration among genes measured in this study. Neuronal
subclusters show both genotype enrichment, and spatial localization, implying
regional population variations induced by 5xFAD and Trem2R47H mutations.


DISCUSSION

Trem2R47H is strongly associated with the development of Late-Onset Alzheimer’s
Disease. Here, we investigated the spatial transcriptomic impacts of this
critical mutation in the context of the 5xFAD mouse model of amyloidosis, as
well as in a WT mouse background. Across 19 coronal slices we profiled over
400,000 cells and examined transcriptome dysregulation in neuronal and glial
cell types. This analysis provides a broad perspective, enabling analysis of
regional and cell type-specific transcriptome dysregulation at the single-cell
level. We improved on previous spatial transcriptomic analyses of Alzheimer’s
disease mouse models, which were generally limited either in imaged area or in
spatial resolution.

To our knowledge, this is the first study to analyze spatial transcriptomics in
Alzheimer’s mouse models across whole coronal sections at the single-cell level.
Our findings can be organized into four major ideas. First, we identified cell
type-specific, plaque-induced differential expression common to both Trem2R47H;
5xFAD and 5xFAD mouse models. Several of these genes have previously been linked
to Alzheimer’s, and in L6b and cortical amygdala excitatory neurons, we
identified differentially regulated gene groups specifically associated with
learning and memory. Second, we identified unique distribution characteristics
for disease associated microglia and astrocytes in relation to plaque density
and structural region, with disease associated microglia and astrocytes with
high concentrations of microglia in the corpus callosum and subcortical regions,
while DAAs were almost exclusively localized to corpus callosum and hippocampus.
Third, we identified consistent transcriptomic alterations across cortical,
hippocampal, and subcortical neuronal populations, linking cortical excitatory
neurons to alterations in BDNF signaling induced by the Trem2R47H mutation.
Finally, we identified neuronal subtypes enriched and depleted in 5xFAD or
Trem2R47H; 5xFAD that are spatially localized with respect to plaque density.
Several of these subtypes show transcriptomic alterations related to learning
and memory.

Previous characterization of Trem2R47H; 5xFAD brains found an initial effect
whereby the presence of the variant impeded the microglial response to plaques
and exacerbated surrounding dystrophic neurites. However, this suppression of
microglial response to plaques subsided at later disease stages, resulting in
expected numbers of plaque-associated microglia, and no significant changes in
DAM gene expression via bulk-tissue RNA-seq between Trem2R47H; 5xFAD and 5xFAD
hippocampuses [22]. This is consistent with our findings here in regions
exhibiting early plaque development (hippocampus, limbic cortex), with
hypothesized delay in regions of low plaque development (midbrain, thalamus,
hypothalamus) due to later development of plaques in these areas, potentially
resulting in a region-specific time-dependent switch in microglia concentration
and activation. Despite a seemingly appropriate microglial response to plaques
at these later stages, this previous study observed unique emergence of an
interferon signature by TREM2R47H, coupled with increased plasma neurofilament
light chain, a marker of neuronal damage [22]. In agreement with this, we found
that the presence of TREM2R47H has significant impacts on gene expression within
neurons. As indicated, both microglia and astrocytes showed much higher impacts
from the 5xFAD transgenes than from the Trem2R47H mutation, resulting in a
larger number of differentially expressed genes, and higher magnitude expression
changes in both 5xFAD dependent comparisons than in the Trem2R47H dependent
comparisons. Of those few genes that were differentially expressed in a
Trem2R47H dependent manner, several are associated with a homeostatic microglial
state (P2ry12, Tmem119), in Trem2R47H; 5xFAD compared with 5xFAD mice. TREM2 is
required to transition microglia from a homeostatic to a DAM state, and the lack
of downregulation of these homeostatic markers is consistent with a partial loss
of TREM2 ability to mediate this transition with the R47H mutation.

Both disease-associated microglia and astrocytes exhibited distinct spatial
distributions, with significant concentrations of both populations in the corpus
callosum (CC), though DAM were distributed more evenly across the brain, while
DAA were restricted almost exclusively to the CC. While proportions of disease
associated astrocytes were consistent between 5xFAD and Trem2R47H; 5xFAD mouse
models, 5xFAD proportions of disease associated microglia were higher than
Trem2R47H; 5xFAD in the dentate gyrus, midbrain, thalamus and hypothalamus,
correlating with plaque density. Previous studies have shown indications of
neuronal loss in 5xFAD mouse models, particularly in lower cortical layers and
the subiculum, though the effect size, statistical significance, and analysis
mechanisms vary widely across the literature. Our analysis did not identify any
statistically significant neuronal loss, however we do note that the previously
identified locations of neuronal loss exhibit remarkably high plaque, DAA and
DAM concentrations [77].

Analysis of plaque proximal transcriptomic dysregulation identified the
prominent disease associated microglia and astrocyte signatures, with some
distance-dependent variations (e.g. P2ry12 was identified as downregulated near
plaques for microglia within 100 µm, but not between cells within 100 µm, and
those 100–500 µm from the nearest plaque, with the reverse true for Tmem119).
Cd74 was not identified as upregulated near plaques but was strongly upregulated
in 5xFAD and Trem2R47H; 5xFAD mice (compared with WT and Trem2R47H
respectively). Upregulation of this gene is thought to precede final
differentiation into DAM states [78], and exhibits homogeneous upregulation
induced by 5xFAD independent of plaque proximity.

Neurons also demonstrated transcriptional alterations commensurate with plaque
proximity, which demonstrate little to no overlap with genotype (5xFAD or
Trem2R47H) induced differentially expressed genes. Of those genes upregulated
near plaques, many are associated with synaptic transmission including receptors
(Grm1, Htr1a, Cnr1), synaptic vesicle traffic (Rph3a), and regulation of
neuronal signaling pathways (Adgra1). Genes downregulated near plaques are
associated with neuronal growth and survival (Ngf, Ntf3), and synaptic
transmission and regulation (Nptx1, Camk2g, Kcnd2, Syt6).

Examining genotype induced differential expression across cortical excitatory
cell types, we identified Fos, Wfs1, and Grm2 as downregulated by the Trem2R47H
mutation and Ntrk2, Bdnf, and Cdh12 as upregulated. Downregulation of Fos and
Grm2 is indicative of a decrease in activity and synaptic transmission, and is
consistent with the LTP impairments observed in these mice at this age [22].
Wfs1 is a marker for a unique neuronal population in the entorhinal cortex that
modulates spatial memory and is implicated in late stage induced hypoactivity in
the hippocampal formation, and is also linked to Tau pathology [67, 79]. Nr2f2
and Bdnf are both critical to the BDNF signaling pathway, which activates ERK
and Akt pathways to maintain neuronal survival and synaptic plasticity [80].

Thalamic excitatory neurons exhibited both 5xFAD and Trem2R47H impacts, and many
differentially expressed genes between WT and 5xFAD mice, including
downregulation of Bdnf, have previously been linked to Alzheimer’s. CA1
excitatory neurons showed the largest number of differentially expressed genes
among non-neocortical neurons, among genes in this panel. However, relatively
few differentially expressed genes were identified between 5xFAD and WT mice.
The only consistent differentially expressed genes induced by the Trem2R47H
mutation were Ntrk2 (upregulated in Trem2R47H and Trem2R47H; 5xFAD) and Fos,
similar to results in cortical excitatory neurons. Inhibitory neurons exhibited
consistent differential expression of Epha10 (upregulated in 5xFAD compared with
WT and downregulated in Trem2R47H; 5xFAD compared with 5xFAD). This gene is part
of the ephrin family and is critically involved in memory formation, with
knockout impacts of related receptors (Epha3/4) resulting in reduction in
context dependent memory [70].

Most neuron types also exhibit genotype-specific enriched and reduced
subclusters, primarily in 5xFAD and Trem2R47H; 5xFAD mice. Several subclusters
exhibit spatial localization, such as the L5 NP subcluster spatially localized
near the subiculum, and the L2 IT subcluster spatially localized in the visual
and retrosplenial cortices.

Together, our spatial transcriptomic analysis of the effect of the Trem2R47H
mutation on transcriptional dysregulation across both cortical and subcortical
brain regions identified plaque and genotype-dependent transcriptional
alterations, cell type-specific transcriptome alterations, and genotype specific
cell sub-types spatially localized across the brain.


DATA AVAILABILITY

Raw and processed data are available at the brain image library
(/bil/lz/kjohnst2/b72faf9d87d7fc00). We will comply with the NIH and MODEL-AD
consortium requirements for data sharing.


CODE AVAILABILITY

Associated Code is available at the brain image library
(/bil/lz/kjohnst2/b72faf9d87d7fc00). We will comply with the NIH and MODEL-AD
consortium requirements for data sharing.


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Download references


ACKNOWLEDGEMENTS

The authors would like to thank the Vizgen team for their assistance in
troubleshooting the MERFISH experimental protocols.


FUNDING

This research was supported by NIH grants (R01 AG082127, U01 AG076791, R01
AG067153, U54 AG054349, RF1 AG065675). Kevin Johnston acknowledges support from
the NIDCD grant T32 DC010775-14. The authors acknowledge the support of the Chao
Family Comprehensive Cancer Center Transgenic Mouse Facility shared resource
supported in part by the National Cancer Institute of the National Institutes of
Health under award number P30CA062203.


AUTHOR INFORMATION


AUTHORS AND AFFILIATIONS

 1. Department of Anatomy and Neurobiology, School of Medicine, University of
    California, Irvine, CA, 92697, USA
    
    Kevin G. Johnston, Bereket T. Berackey, Zhiqun Tan & Xiangmin Xu

 2. Department of Biomedical Engineering, University of California, Irvine, CA,
    92697, USA
    
    Bereket T. Berackey & Xiangmin Xu

 3. Department of Neurobiology and Behavior, Charlie Dunlop School of Biological
    Sciences, University of California, Irvine, CA, 92697, USA
    
    Kristine M. Tran & Kim N. Green

 4. Department of Cognitive Science, University of California, San Diego, CA,
    92037, USA
    
    Alon Gelber & Eran A. Mukamel

 5. Department of Statistics, School of Computer and Information Science,
    University of California, Irvine, CA, 92697, USA
    
    Zhaoxia Yu

 6. Center for Neural Circuit Mapping, University of California, Irvine, CA,
    92697, USA
    
    Zhaoxia Yu, Zhiqun Tan & Xiangmin Xu

 7. Department of Developmental and Cell Biology, University of California,
    Irvine, CA, 92697, USA
    
    Grant R. MacGregor

 8. Institute for Memory Impairments and Neurological Disorders (UCI MIND),
    Irvine, USA
    
    Grant R. MacGregor, Zhiqun Tan, Kim N. Green & Xiangmin Xu

 9. Department of Molecular Biology and Biochemistry School of Biological
    Sciences, University of California, Irvine, CA, 92697, USA
    
    Zhiqun Tan

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 1.  Kevin G. Johnston
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 3.  Kristine M. Tran
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 6.  Grant R. MacGregor
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 7.  Eran A. Mukamel
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 10. Xiangmin Xu
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CONTRIBUTIONS

Xiangmin Xu, Zhiqun Tan and Kim N. Green designed experiments. Kevin Johnston
performed analysis and computation, created figures and drafted the manuscript.
Zhiqun Tan performed all MERFISH experiments. Kristine M. Tran, Grant R.
MacGregor and Kim N. Green provided animals for the experiment, as well as
interpretation of biological results. Alon Gelber, Zhaoxia Yu, and Eran Mukamel
provided statistical analysis support and advice. Bereket Berackey provided
computational support and developed the cell segmentation pipeline. Xiangmin Xu
oversaw the project. All authors reviewed and revised the manuscript and consent
to publication.


CORRESPONDING AUTHOR

Correspondence to Xiangmin Xu.


ETHICS DECLARATIONS


COMPETING INTERESTS

The authors declare no competing interests.


ETHICS APPROVAL AND CONSENT TO PARTICIPATE

This study did not involve any human subjects. All animal care and related
experimental procedures were conducted following the highest ethical standards
and were approved by the UC Irvine Institutional Animal Care and Use Committee.
All animals were bred and raised by the Transgenic Mouse Facility at UCI under a
regular light/dark (12 h/12 h) cycle with ad libitum access to food and water.


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Johnston, K.G., Berackey, B.T., Tran, K.M. et al. Single-cell spatial
transcriptomics reveals distinct patterns of dysregulation in non-neuronal and
neuronal cells induced by the Trem2R47H Alzheimer’s risk gene mutation. Mol
Psychiatry (2024). https://doi.org/10.1038/s41380-024-02651-0

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 * Received: 27 February 2024

 * Revised: 20 June 2024

 * Accepted: 26 June 2024

 * Published: 05 August 2024

 * DOI: https://doi.org/10.1038/s41380-024-02651-0


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SUBJECTS

 * Cell biology
 * Molecular biology
 * Neuroscience

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 * Abstract
 * Introduction
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 * Fig. 1: MERFISH spatial transcriptomics enables spatial variation analysis of
   the transcriptome at the cell type level.
   
   View in articleFull size image

 * Fig. 2: Spatial and transcriptomic analysis of coronal brain slices enables
   analysis of the spatial distribution of individual genes.
   
   View in articleFull size image

 * Fig. 3: Machine learning enables accurate identification of plaque locations
   across brain slices.
   
   View in articleFull size image

 * Fig. 4: Aβ plaque proximity causes transcriptomic dysregulation in both glia
   and neuronal cell types.
   
   View in articleFull size image

 * Fig. 5: Microglia and astrocytes exhibit 5xFAD induced transcriptome
   alterations.
   
   View in articleFull size image

 * Fig. 6: Cortical neurons exhibit consistent Trem2 associated transcriptomic
   variations and spatially localized genotype biased subclusters.
   
   View in articleFull size image

 * Fig. 7: CA3 excitatory neurons exhibit significant 5xFAD induced
   transcriptional differences.
   
   View in articleFull size image

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Molecular Psychiatry (Mol Psychiatry) ISSN 1476-5578 (online) ISSN 1359-4184
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