DOI: 10.5061/dryad.3tx95x6rq
title: Microglial replacement in a Sandhoff disease mouse model reveals myeloid-derived β- hexosaminidase is necessary for neuronal health

description: Lysosomal storage disorders (LSDs) are a large disease class involving
 lysosomal dysfunction, often resulting in neurodegeneration. Sandhoff
 disease (SD) is an LSD caused by a deficiency in the β subunit of the
 β-hexosaminidase enzyme (Hexb). Although Hexb expression in the brain is
 specific to microglia, SD primarily affects neurons. To investigate how a
 microglial gene is involved in neuronal homeostasis, here we show that
 β-hexosaminidase is secreted by microglia and integrated into the
 lysosomal compartment of neurons. To assess therapeutic relevance, we
 treat the Hexb-/- SD mouse model with bone marrow transplant and
 colony-stimulating factor 1 receptor inhibition, which broadly replaces
 Hexb-/- microglia with Hexb-sufficient cells. Microglial replacement
 reverses apoptotic gene signatures, improves behavior, restores
 β-hexosaminidase enzymatic activity and Hexb expression, prevents
 substrate buildup, and normalizes neuronal lysosomal phenotypes,
 underscoring the critical role of myeloid-derived β-hexosaminidase in
 maintaining neuronal health and establishing microglial replacement as a
 potential LSD therapy.
description: <strong>Spatial transcriptomic & proteomic
 analysis</strong> Section preparation: One day
 prior to experiment, PFA-fixed brain hemispheres were embedded in optimal
 cutting temperature (OCT) compound (Tissue-Tek, Sakura Fintek, Torrance,
 CA), and 10 μm sagittal sections were cut using a cryostat (CM1950,
 LeicaBiosystems, Deer Park, IL). Six hemibrains were mounted onto VWR
 Superfrost Plus slides (Avantor, 48311–703) and kept at −80°C overnight.
 For <em>Hexb</em><sup>-/-</sup> BMT groups and the
 WT control group, n=3 mice per experimental condition were utilized
 (wild-type control, <em>Hexb</em><sup>-/-</sup>
 control, <em>Hexb</em><sup>-/-</sup> BMT,
 <em>Hexb</em><sup>-/-</sup> BMT + CSF1Ri) for
 transcriptomics and proteomics. When selecting representative brains, we
 considered BMDM infiltration levels from both
 <em>Hexb</em><sup>-/-</sup> BMT groups, choosing
 brains with similar total forebrain GFP^+^ staining to group averages.
 Tissue was processed in accordance with the Nanostring CosMx fresh-frozen
 slide preparation manual for RNA and protein assays (NanoString
 University). Slide treatment, RNA, day 1:** Slides were
 removed from -80°C and baked at 60°C for 30 min. Slides were then
 processed for CosMx: three 1X washes PBS for 5 minutes each, 4% sodium
 dodecyl sulfate (SDS; CAT#AM9822) for 2 minutes, three 1X PBS washes for 5
 minutes each, 50% ethanol for 5 minutes, 70% ethanol for 5 minutes, and
 two washes with 100% ethanol for 5 minutes each before allowing slides to
 air dry for 10 minutes at room temperature. Antigen retrieval was
 performed using a pressure cooker maintained at 100°C for 15 min in
 preheated 1X CosMx Target Retrieval Solution (Nanostring, Seattle, WA).
 Slides were then transferred to DEPC-treated water (CAT#AM9922) and washed
 for 15 seconds, incubated in 100% ethanol for 3 minutes, and air dried at
 room temperature for 30 minutes. Slides were incubated with digestion
 buffer (3 μg/mL Proteinase K in 1X PBS; Nanostring) for tissue
 permeabilization, then washed 2 times in 1X PBS for 5 minutes. Fiducials
 for imaging were diluted to 0.00015% in 2X SSC-T and incubated on slides
 for 5 minutes. Following fiducial treatment, slides were protected from
 light at all times. Tissues were then post-fixed with 10% neutral buffered
 formalin (NBF; CAT#15740) for 1 minute, washed twice with NBF Stop Buffer
 (0.1M Tris-Glycine Buffer, CAT#15740) for 5 minutes each, and washed with
 1x PBS for 5 minutes. Next, NHS-Acetate (100 mM; CAT#26777) mixture was
 applied to each slide and incubated at room temperature for 15 minutes.
 Slides were washed twice with 2X SSC for 5 minutes each. Slides were then
 incubated for 16–18 hours in a hybridization oven at 37°C with a modified
 1000-plex Mouse Neuroscience RNA panel (Nanostring) for <em>in
 situ</em> hybridization with the addition of an rRNA segmentation
 marker. <em>Slide treatment, RNA, day
 2:</em> Following <em>in situ</em> hybridization, slides
 were washed twice in pre-heated stringent wash solution (50% deionized
 formamide [CAT#AM9342], 2X saline-sodium citrate [SSC; CAT#AM9763]) at
 37°C for 25 minutes each, then washed twice in 2X SSC for 2 minutes each.
 Slides were then incubated with DAPI nuclear stain for 15 minutes, washed
 with 1X PBS for 5 minutes, incubated with GFAP and histone cell
 segmentation markers for 1 hour, and washed three times in 1X PBS for 5
 minutes each. Flow cells were adhered to each slide to create a fluidic
 chamber for spatial imaging. Slides were loaded into and processed
 automatically with the CosMx instrument. Approximately 300 fields of view
 (FOVs) were selected on each slide, capturing hippocampal, corpus
 callosum, upper thalamic, upper caudate, and cortical regions for each
 section. Slides were imaged for approximately 7 days and data were
 automatically uploaded to the Nanostring AtoMx online platform. Pipeline
 pre-processed data was exported as a Seurat object for analysis with R
 4.3.1 software. Side treatment, protein, day 1: Slides
 were removed from -80°C and baked at 60°C for 30 min, then washed three
 times with 1X Tris Buffered Saline with Tween (TBS-T; CAT#J77500.K2) for 5
 minutes each. Antigen retrieval was performed using a pressure cooker held
 at 80°C in pre-heated Tris-EDTA buffer (10 mM Tris Base [CAT#10708976001],
 1 mM EDTA solution, 0.05% Tween 20, pH 9.0) for 7 minutes. Following
 antigen retrieval, slides were allowed to cool to room temperature for 5
 minutes, then washed three times in 1X TBS-T for 5 minutes each. Slides
 were incubated with Buffer W (Nanostring) for 1 hour at room temperature.
 Slides were then incubated for 16-18 hours at 4°C with the CosMx 64-plex
 protein panel and segmentation markers (GFAP, IBA1, NEUN, and
 S6). Side treatment, protein, day 2: Following
 incubation, slides were washed three times with 1X TBS-T for 10 minutes
 each, then washed with 1X PBS for 2 minutes. Fiducials for imaging were
 diluted to 0.00005% in 1X TBS-T and incubated on the slide for 5 minutes.
 Slides were the washed with 1X PBS for 5 minutes, incubated in 4% PFA for
 15 minutes, and washed three times with 1X PBS for 5 minutes each. Slides
 were incubated with DAPI nuclear stain for 10 minutes, then washed twice
 with 1X PBS for 5 minutes each. Slides were then incubated with 100 mM
 NHS-Acetate for 15 minutes and washed with 1X PBS for 5 minutes. Flow
 cells were adhered to each slide to create a fluidic chamber for spatial
 imaging. Slides were loaded into and processed automatically with the
 CosMx instrument. Approximately 600 FOVs were selected per slide,
 capturing each full section. Slides were imaged for ~6 days data were
 automatically uploaded to the Nanostring AtoMx online platform. Pipeline
 pre-processed data was exported as a Seurat object for analysis with R
 4.3.1 software. Spatial transcriptomics data analysis:
 Spatial transcriptomics datasets were processed as previously described
 (Tran et al., <em>Mol Neurodegen</em> 2025). Principal
 component analysis (PCA) and uniform manifold approximation and projection
 (UMAP) analysis were performed to reduce the dimensionality of the dataset
 and visualize clusters in space. Unsupervised clustering at 1.0 resolution
 yielded 39 clusters for the WT control versus
 <em>Hexb</em><sup>-/-</sup> control dataset and 38
 clusters for the dataset which included WT controls,
 <em>Hexb</em><sup>-/-</sup> controls,
 <em>Hexb</em><sup>-/-</sup> BMT, and
 <em>Hexb</em><sup>-/-</sup> BMT + CSF1Ri. Clusters
 were annotated with a combination of automated and manual approaches: 1)
 label annotations from the Allen Brain Atlas single-cell RNA-seq reference
 dataset (for cortex and hippocampus) were projected onto our spatial
 transcriptomics dataset (Yao et al., *Cell *2021) and 2) cluster
 identities were further refined via manual annotation based on gene
 expression of known marker genes and location in XY space. Cell proportion
 plots were generated by first plotting the number of cells in each broad
 cell type, then scaling to 1. normalized percentages for each group,
 calculated by dividing the number of cells in a given cell type-group pair
 by the total number of cells in that group, and 2. dividing by the sum of
 the proportions across the cell type to account for differences in sample
 sizes. Differential gene expression analysis per cell type between groups
 was performed on scaled expression data using MAST to calculate the
 average difference (Finak et al., <em>Genome Biol</em> 2015),
 defined as the difference in log-scaled average expression between the two
 groups for each broad cell type. DEG scores were calculated between group
 pairs for each subcluster by summing the absolute
 log<sub>2</sub> fold change values of all genes with
 statistically significant gene (i.e., p<sub>adj</sub> &lt;
 0.05) differential expression patterns between two groups. Data
 visualizations were generated using ggplot2 3.4.4 (Wickham
 2016).
description: # Microglial replacement in a Sandhoff disease mouse model reveals
 myeloid-derived β-hexosaminidase is necessary for neuronal health
 [https://doi.org/10.5061/dryad.3tx95x6rq](https://doi.org/10.5061/dryad.3tx95x6rq) ## Description of the data and file structure These data were collected to assess differences between experimental treatment strategies in the Sandhoff disease model *Hexb* knockout mice using CosMx spatial transcriptomic and proteomic analyses. One spatial transcriptomics dataset is from only wildtype control and *Hexb* knockout control mice. One dataset is a spatial transcriptomics experiment performed on wildtype control, *Hexb* knockout control, *Hexb* knockout treated with bone marrow transplant (BMT), and *Hexb* knockout treated with BMT and colony-stimulating factor 1 receptor (CSF1R) inhibition, which results in broad replacement of microglia with bone marrow-derived macrophages/monocytes. The final dataset is a spatial proteomics experiment performed on these same four groups (WT control, *Hexb* knockout control, *Hexb* knockout BMT, and *Hexb* knockout BMT + CSF1R inhibition). We have submitted all processed RDS files (HexbKO_wt_vs_con_with_allenref_labels_public.rds, HexbKO_full_with_allenref_labels_public.rds, HexbKO_protein_seurat_w_group_assignments.rds), analyzed using the R package Seurat. Sample metadata are stored in [seurat@meta.data](mailto:seurat@meta.data). For spatial proteomics, we have included the .csv files containing the parameters used to perform automated cell typing with the CELESTA algorithm (mouse_signature_matrix.csv, mouse_tuning_params.csv). #### **Files and variables** ##### **Single-cell spatial transcriptomics datasets** #### Files: HexbKO_wt_vs_con_with_allenref_labels_public.rds, HexbKO_full_with_allenref_labels_public.rds Row names of metadata (accessed using rownames([seurat@meta.data](mailto:seurat@meta.data))) contain unique identifiers for each single cell, formatted as c_[slide]*[fov]*[cell]. Additional metadata columns are described below: * fov: Field Of View (FOV) the cell is in * Area: Number of pixels assigned to a given cell * AspectRatio: Width divided by height * Width: Cell’s maximum length in x dimension (pixels) * Height: Cell’s maximum length in y dimension (pixels) * Mean.DAPI, Mean.Histone, Mean.G, Mean.GFAP: Mean fluorescence intensity within a given cell (AU) * Max.DAPI, Max.Histone, Max.G, Max.GFAP: Max fluorescence intensity within a given cell (AU) * Run_Tissue_name: Flowcell name * x_FOV_px: x position of the cell center within the FOV, measured in pixels * y_FOV_px: y position of the cell center within the FOV, measured in pixels * slide_ID_numeric: SlideID * cell_ID: cell identification number, formatted as c_[slide]*[fov]*[cell] * x_slide_mm: x position of the cell center within the slide, measured in mm * y_slide_mm: y position of the cell center within the slide, measured in mm * nCount_RNA: Number of RNA counts * nFeature_RNA: Number of unique RNA targets * nCount_negprobes: Number of Negative counts * nFeature_negprobes: Number of unique Negative targets * Area.um2: Area of cell (um^2) * nCount_SCT: SCT-normalized number of unique RNA transcripts per cell * nFeature_SCT: SCT-normalized number of features (genes) per cell * SCT_snn_res.1: Clustering from FindMarkers() at resolution = 1.0 * seurat_clusters: Final clusters from FindMarkers() (which were used to annotate) * predicted.celltype: label annotations from an Allen Brain Atlas single-cell RNA-seq reference dataset for cortex and hippocampus ([https://doi.org/10.1016/j.cell.2021.04.021](https://doi.org/10.1016/j.cell.2021.04.021)), predicting cell identity * predicted.celltype.score: estimated accuracy of Allen Brain cell type prediction * manual_clusters: manual cell type annotation based on Allen Brain cell type prediction, marker genes, and location in space * manual_clusters_collapsed: combination of manual cell type annotation into more broad cell types while maintaining individual cell identities (i.e., VLMC 1, 2, etc -&gt; VLMC) * collapsed_simple: simplification of manual cell type annotation into broad cell types (i.e., VLMC -&gt; Vascular; Pvalb, L2_3 IT CTX -&gt; Inhibitory Neuron) * group: Genotype name * sample_n: Sample name ##### **Single-cell spatial proteomics dataset** #### File: HexbKO_protein_seurat_w_group_assignments_public.rds Row names of metadata (accessed using row names([seurat@meta.data](mailto:seurat@meta.data))) contain unique identifiers for each single cell, formatted as c_[slide]*[fov]*[cell]. Additional relevant metadata columns are described below: * fov: Field Of View (FOV) the cell is in * Area: Number of pixels assigned to a given cell * AspectRatio: Width divided by height  * x_FOV_px: x position of the cell center within the FOV, measured in pixels * y_FOV_px: y position of the cell center within the FOV, measured in pixels * Width: Cell’s maximum length in x dimension (pixels) * Height: Cell’s maximum length in y dimension (pixels) * Mean.DAPI: Mean fluorescence intensity within a given cell (AU) * Max.DAPI: Max fluorescence intensity within a given cell (AU) * Run_Tissue_name: Flowcell name * slide_ID_numeric: SlideID * x_slide_mm: x position of the cell center within the slide, measured in mm * y_slide_mm: y position of the cell center within the slide, measured in mm * nCount_RNA: Mean fluorescence intensity ("RNA" is a misnomer) * nFeature_RNA: Number of unique proteins detected ("RNA" is a misnomer) * nCount_negprobes: Number of Negative counts * nFeature_negprobes: Number of unique Negative targets * Area.um2: Area of cell (um^2) * celesta_R1: Cell type annotations from round 1 of CELESTA cell typing * celesta_R2: Cell type annotations from round 2 of CELESTA cell typing (All microglia are further classified into DAM or homeostatic) * celesta_final: Final annotations from CELESTA * celesta_broad: Broad cell type annotations (e.g., all neurons grouped together) * celesta_cell_type_n: Number associated with cell type (from mouse_signature_matrix.csv) * sex: Sample sex * group: Genotype name * sample_n: Sample name ##### ***mouse_signature_matrix.csv*** User-defined cell-type signature matrix. (1) The first column has to contain the cell types to be inferred (2) The second column has the lineage information for each cell type. The lineage information has three numbers connected by “_” (underscore). The first number indicates the round. Cell types with the same lineage level are inferred at the same round. The increasing number indicates increased cell-type resolution. (3) Starting from column three, each column is a protein marker. If the protein marker is known to be expressed for that cell type, then it is denoted by “1”. If the protein marker is known not to be expressed for a cell type, then it is denoted by “0”. If the protein marker is irrelevant or uncertain to express for a cell type, then it is left blank. For example, CD11c is expressed in some but not all microglia, so it is left blank for All_Microglia.  ## Code/software This data was produced using the RNA Quality Control Module from Nanostring's AtomX software and analyzed and annotated in R using the following open-source packages: Seurat 5.0.1 SCTransform, ggplot2, and MAST (Model-based Analysis of Single-cell Transcriptomes).