CD8+ T cells induce interferon-responsive oligodendrocytes and microglia in white matter aging

A hallmark of nervous system aging is a decline of white matter volume and function, but the underlying mechanisms leading to white matter pathology are unknown. In the present study, we found age-related alterations of oligodendrocyte cell state with a reduction in total oligodendrocyte density in aging murine white matter. Using single-cell RNA-sequencing, we identified interferon (IFN)-responsive oligodendrocytes, which localize in proximity to CD8+ T cells in aging white matter. Absence of functional lymphocytes decreased the number of IFN-responsive oligodendrocytes and rescued oligodendrocyte loss, whereas T-cell checkpoint inhibition worsened the aging response. In addition, we identified a subpopulation of lymphocyte-dependent, IFN-responsive microglia in the vicinity of the CD8+ T cells in aging white matter. In summary, we provide evidence that CD8+ T-cell-induced, IFN-responsive oligodendrocytes and microglia are important modifiers of white matter aging.

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Software and code
Policy information about availability of computer code Data collection 1. Confocal images: all images of mouse brain sections were acquired using a Leica TCS SP5 or Zeiss LSM900 confocal microscope and were processed with Imaris (64x version 9.2.0) and ImageJ 1.41 image processing softwares. A Leica DMI6000 widefield microscope was used for cell culture experiments 2. Single-cell RNA-seq data collection for the Smart-seq2 dataset: BCL files were demultiplexed with the bcl2fastq software from Illumina. After quality-control with FastQC, reads were aligned using rnaSTAR to the GRCm38 (mm10) genome with ERCC synthetic RNA added. Read counts were collected using the parameter "quantMode GeneCounts" of rnaSTAR and using the unstranded values.

March 2021
Data Policy information about availability of data All manuscripts must include a data availability statement. This statement should provide the following information, where applicable: -Accession codes, unique identifiers, or web links for publicly available datasets -A description of any restrictions on data availability -For clinical datasets or third party data, please ensure that the statement adheres to our policy The datasets we use (scRNA-seq) are deposited at GEO (NCBI) under accession number GSE202579. Datasets re-analyzed in this study include data from GSE166548, GSE138891 and GSE132042. The rest of the data included in this study are available within the paper as data source files in the supplementary information.
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Replication
For all mouse experiments, 3-6 mice per genotype were analyzed. For histological analysis, 3-6 random region of interest (ROIs) per each brain section were taken and three random brain sections per animal were quantified to account for variability within the biological sample. Replicaion was succesful for all conditions reported.
Randomization The allocation of samples including brain sections was random.

Blinding
All data acquisition and analysis fwere done in a blinded manner. The experimentor was unblinded when preparing samples from IFNg because of the obvious differences in the phenotype.

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Software
The SH800S software for the data collection. FlowJo was used for the flow cytometry data analysis.

Cell population abundance
During single-cell library preparation we have confirmed the quality of the single-cells via qPCR assay (more details in methods section).
Gating strategy CNS cells were gated for singlets by using FSC-A and FSC-H, followed by gating for living cell ( SYTOX Blue negative population, fixable viability dye), then CD11b-cells were sorted.
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