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Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency

Citation

Chen, Elizabeth; Jones, Robert; Clarke, Michael; Quake, Stephen (2022), Single-Cell RNA-sequencing of neural precursor cells from an Alzheimer's mouse model, wild-type mice, and Alzheimer's mice rescued with Usp16 haploinsufficiency, Dryad, Dataset, https://doi.org/10.5061/dryad.mpg4f4qz0

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disease observed with aging that represents the most common form of dementia. To date, therapies targeting end-stage disease plaques, tangles, or inflammation have limited efficacy. Therefore, we set out to identify an earlier targetable phenotype. Utilizing a mouse model of AD we found that cell intrinsic neural precursor cell (NPC) dysfunction precedes widespread inflammation and amyloid plaque pathology, making it one of the earlier defects in the evolution of the disease. We demonstrate that reversing impaired NPC self-renewal via genetic reduction of USP16, a histone modifier and critical physiological antagonist of the Polycomb Repressor Complex 1, can prevent downstream cognitive defects and decrease astrogliosis in vivo. To delineate potential self-renewal pathways that might contribute to the defect and rescue of Tg-SwDI NPCs and Tg-SwDI/Usp16+/- NPCs, respectively, we performed single-cell RNA-seq and gene set enrichment analysis (GSEA) on lineage depleted primary FACS-sorted CD31-CD45-Ter119-CD24- NPCs from Tg-SwDI, WT, and Tg-SwDI/Usp16+/- mice at 3-4 months and 1 year of age. Using the GSEA Hallmark gene sets, we found only three gene sets that were enriched in Tg-SwDI mice over WT mice and rescued in the Tg-SwDI/Usp16+/- mice at both ages: TGF-ß pathway, oxidative phosphorylation, and Myc Targets. The TGF-ß pathway consistently had the highest normalized enrichment score in pairwise comparisons between Tg-SwDI vs WT and Tg-SwDI vs Tg-SwDI/Usp16+/- of the three rescued pathways. These data suggest that USP16 may regulate neural precursor cell function in part through the BMP pathway.

Methods

The subventricular zone of 4 mice from each genotype (AD mouse model: Tg-SwDI, wild type mice, AD mouse model haploinsufficient for Usp16: Tg-SwDI/Usp16+/-, and Usp16 haploinsufficient mice: Usp16+/-) was micro-dissected and tissue digested using Liberase DH (Roche) and DNAse I (250U/ml) at 37°C for 20 minutes followed by trituration. Digested tissue was washed in ice-cold HBSS without calcium and magnesium, filtered through a 40-μm filter, and then stained with the following antibodies for 30 minutes: PacBlue-CD31 (Biolegend), PacBlue-CD45 (Biolegend), PacBlue-Ter119 (Biolegend), and FITC-CD24 (Biolegend). Sytox Blue was used for cell death exclusion and samples were sorted into 384 well plates prepared with lysis buffer using the Sony Sorter. cDNA synthesis was performed using the Smart-seq2 protocol [1,2]. Illumina sequencing libraries were prepared according to the protocol in the Nextera XT Library Sample Preparation kit (Illumina, FC-131-1096). Each well was mixed with 0.8 μl Nextera tagmentation DNA buffer (Illumina) and 0.4 μl Tn5 enzyme (Illumina), then incubated at 55°C for 10 min. The reaction was stopped by adding 0.4 μl “Neutralize Tagment Buffer” (Illumina) and spinning at room temperature in a centrifuge at 3220xg for 5 min. Indexing PCR reactions were performed by adding 0.4 μl of 5 μM i5 indexing primer, 0.4 μl of 5 μM i7 indexing primer, and 1.2 μl of Nextera NPM mix (Illumina). PCR amplification was carried out on a ProFlex 2x384 thermal cycler using the following program: 1. 72°C for 3 minutes, 2. 95°C for 30 seconds, 3. 12 cycles of 95°C for 10 seconds, 55°C for 30 seconds, and 72°C for 1 minute, and 4. 72°C for 5 minutes. Following library preparation, wells of each library plate were pooled using a Mosquito liquid handler (TTP Labtech). Pooling was followed by two purifications using 0.7x AMPure beads (Fisher, A63881). Library quality was assessed using capillary electrophoresis on a Fragment Analyzer (AATI), and libraries were quantified by qPCR (Kapa Biosystems, KK4923) on a CFX96 Touch Real-Time PCR Detection System (Biorad). Plate pools were normalized to 2 nM and equal volumes from 10 or 20 plates were mixed together to make the sequencing sample pool. PhiX control library was spiked in at 0.2% before sequencing. Single-cell libraries were sequenced on the NovaSeq 6000 Sequencing System (Illumina) using 2 x 100bp paired-end reads and 2 x 8bp or 2 x 12bp index reads with a 300-cycle kit (Illumina 20012860).Sequences were collected from the sequencer and de-multiplexed using bcl2fastq version 2.19.0.316. Reads were aligned using to the mm10plus genome using STAR version 2.5.2b with parameters TK. Gene counts were produced using HTSEQ version 0.6.1p1 with default parameters, except ‘stranded’ was set to ‘false’, and ‘mode’ was set to ‘intersection-nonempty’. Four biological replicates from each genotype (Tg-SwDI, WT, Tg-SwDI/Usp16+/-, and Usp16+/-) and at each age were combined for the single-cell RNA-seq experiment (16 samples per age group).

Usage Notes

Two raw counts datasets are supplied: one is a gene counts table of 3-4 month old mice (youngrawcounts.csv) and the other is a gene counts table of 1 year old mice (oldrawcounts.csv). There are two associated metadata files for the raw counts files (youngrawcountsmetadata.csv and oldrawcountsmetadata.csv) that detail the mouse ID, plate number, well number, and genotype of the mouse. Alzheimers_SourceFile.Rmd is an R notebook that takes the raw counts and plots analysis plots including TSNE plots and volcano plots. In addition, files needed to load into GSEA include two gct files with log normalized gene counts and two cls files (phenotype labels) to be able to do cross-genotype comparisons. For our study, GSEA with the Hallmarks gene sets was run with standard parameters: 1000 permutations of type phenotype, with no collapsing to gene symbols, and weighted enrichment. Gene sets were considered significantly enriched if FDR<25%. Finally, two csv files are provided detailing differentially expressed genes calculated using DESeq2 between Tg-SwDI and WT mice at 3-4 month old and 1 year old, along with base mean expression, log2FC difference of Tg-SwDI over WT, and adjusted p value. All files are detailed in the README file.

Funding

Chan Zuckerberg Biohub

California Institute of Regenerative Medicine

NIH, Award: R01AG059712