Study participants
Samples were acquired through the Stanford Brain Rejuvenation Program, the NIA funded Stanford Alzheimer’s Disease Research Center (ADRC), the University of California at San Francisco ADRC and the University of California at San Diego ADRC. Collection of CSF was approved by the Institutional Review Board of each university; written consent was obtained from all subjects. A total of 34 living subjects were used in this study, 24 of which were used for scRNA-TCRseq. The 24 subjects included 8 healthy controls, 4 patients with AD, 5 with MCI, and 7 with PD.
Cryopreservation of CSF cells
CSF was collected by lumbar puncture, then centrifuged at 300 rcf for 10 minutes at 4 ◦C to pellet immune cells. Importantly, CSF samples were checked for blood contamination by examining the pellet for the presence of red blood cells by eye. An example of a CSF sample contaminated with blood is shown in Supplemental Figure 1a. Note that cells should remain at 4 ◦C until they are further processed, but it is best to freeze the cells as quickly as possible to limit cell death. The supernatant (cell free CSF) was aliquoted, carefully leaving behind 100 μl of CSF with the pelleted cells. 100 μl of CSF was left so that cells were concentrated enough for counting and viability measurements. The pelleted cells were then gently resuspended in the 100 μl CSF and 10 μl of resuspended cells were then removed for counting. Importantly, cells were gently resuspended by first tapping the bottom of the tube and then gently triturating 10 times, making sure not to touch the pipette tip to the edge of the tube. Then 10 μl CSF was removed and mixed with 10 μl trypan blue to assess red blood cell content and viability. Cells were then visualized on a TC20 automated cell counter (BioRad) and cell number, viability and the presence or absence of red blood cells was recorded. CSF samples contaminated with blood were discarded. The resuspended cells were then mixed with 900 μl Recovery Cell Culture Freezing Medium (Thermo Fisher Scientific). This medium is an optimized version of the typical freezing medium, containing high-glucose Dulbecco's Modified Eagle Medium with 10% serum and 10% dimethyl sulfoxide. We utilized this medium because it is quality tested for pH, osmolality, sterility, and endotoxin and each lot is quality tested on CHO-K1 cells. The freezing medium was first thawed at 37 ◦C, aliquoted, and stored at -20 ◦C. Before use, the medium was thawed at 37 ◦C and kept on ice. After each aliquot was thawed, the freezing medium was stored at 4 ◦C for up to one month. All samples were frozen overnight at −80 ◦C in a Mr. Frosty freezing container (Thermo Fisher Scientific) and transferred the following day to liquid nitrogen for storage. CSF cells were stored in liquid nitrogen on average 266 days.
Preparation of frozen CSF cells for analysis
CSF cells were thawed at 37 ◦C in a water bath with the media submerged and the top of the tube out of the water. Cells were kept in the water bath for as little as possible and removed when the media was nearly completely thawed. The cells were then removed and gently pipetted into a 5 mL flow cytometry tube containing 3 mL pre-warmed (37 ◦C) sorting buffer (PBS with 0.04% bovine serum albumin (BSA)). The tube was then rinsed once with the sorting buffer and placed into the same flow cytometry tube. Cells were then centrifuged at 350 rcf for ten minutes. The supernatant was removed, and cells were resuspended in 500 μl sorting buffer. ½ μl of Sytox Red (Thermo Fisher Scientific) was added to the sample immediately before sorting by flow cytometry. Live cells were sorted into 1.5 mL Eppendorf tubes containing 750 uL sorting buffer. Once all the samples were sorted, cells were spun at 350 rcf at 4 ◦C in a spinning bucket centrifuge for 7 minutes. The supernatant was then removed, leaving behind 10 μl. 10 μl was left behind to resuspend the CSF cells and load the entire volume for droplet scRNA-seq.
Drop-seq of CSF cells
Chromium Single Cell 5’ Library & Gel Bead kit, Chromium Single Cell 5’ Library construction kit, Chromium Single Cell A Chip Kit, and Chromium i7 Multiplex kit (10X Genomics) were used for scRNAseq of CSF cells. We followed 10x Genomic’s User Guide for library construction. The only change we made to their protocol was in Step 1, GEM Generation & Barcoding. The user guide recommends loading a certain volume of cell suspension stock depending on the concentration of the cell suspension and the user’s desired cell recovery. However, because CSF contains such low cell numbers, we loaded all the cells that were resuspended in 10 μl of sorting buffer. We then added the 10 μl of cell suspension and 21.7 μl of nuclease free water, which results in the same total volume of cell suspension/water that the user guide recommends. After library construction, libraries were sequenced by Novogene on a Novoseq S4 sequencer and FASTQ files were generated by Novogene. Cell Ranger v.3.0.2 was used to generate gene-expression matrices for CSF cells. Reads from the 10X v.2 5′ paired library were mapped to the human genome build GRCh38 3.0.0. The 5′ gene-expression libraries were then analyzed with the Cell Ranger count pipeline and the resulting expression matrix was used for further analysis in the Seurat package v.3.0.
Seurat clustering of CSF cells
Individual sample expression matrices were loaded into R using the function Read10x under the Matrix package v.1.2-15. The expression matrix for each sample was merged into one Seurat object using the CreateSeuratObject and MergeSeurat functions. The Seurat package v.3.0 was used for filtering, variable gene selection, normalization, scaling, dimensionality reduction, clustering and visualization. Genes were excluded if they were expressed in fewer than 10 cells and cells were excluded if they expressed fewer than 200 genes. Cells that expressed more than 1,600 genes, more than 6,000 UMIs and more than 10% mitochondrial genes were excluded from the analysis. The sctransform normalization method was used to normalize, scale, select variable genes and regress out sequencing and experimental batch, mitochondrial mapping percentage, number of UMIs, and number of genes. After filtering and normalization, there were 26,797 cells and 14,953 genes. Following PCA, 5 principle components were selected for clustering tSNE dimensionality reduction.
Narcolepsy patient TCR sequences
All Narcolepsy patient TCR sequences were acquired from the Latorre D, et al 2018 study (15).
Calculation of Levenshtein similarities
L-sim scores were calculated using the levenshteinSim function in the RecordLinkage package for R (24). L-sim calculation incorporates the Levenshtein distance algorithm, which quantifies the number of edits, deletions, or insertions required to make two strings identical. L-sim includes a string length normalization and transformation of the final value to be between 0 and 1, with 1 representing identical TCR sequences:
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)
TCR network visualization
TCR networks that show connections between similar TCRs organized by patient IDs and diagnosis groups were generated using the qgraph function in the qgraph package for R (25). Analysis from Figure 3b-d include only clonal TCRs with unambiguous alpha and beta chains. Supplementary Figures 4 and 5 include only clonal TCRs with unambiguous beta chains.
TCR motif analysis
A custom script was used to identify motifs in our TCR beta chains. Only clonal TCRs with unambiguous beta chains were included for analysis. Identified motifs were searched in the McPAS-TCR database, a manually curated database of TCR sequences found to be associated with pathological conditions in mice and humans (21).