TITLE: Single-cell RNA-seq
AUTHORS: patricia.garcia

[DESCRIPTION]
Harvesting and performing single-cell RNA-seq.

[STEPS]
SECTION: Harvesting cells for single-cell RNA-sequencing
1. Samples were trypsinised or scraped from the culture surface and placed in a 15mL conical tube.
SECTION: Harvesting cells for single-cell RNA-sequencing
2. Tubes were centrifuged at 800g in a refrigerated centrifuge for 5 minutes, and the culture media was decanted.
SECTION: Harvesting cells for single-cell RNA-sequencing
3. The pellet was resuspended in 10mL chilled PBS per tube by pipetting, then centrifuged again using the above parameters before decanting the PBS.
SECTION: Harvesting cells for single-cell RNA-sequencing
4. Pellets were resuspended in 1mL PBS, and 100,000 cells were transferred to a 1.5mL falcon tube.
SECTION: Harvesting cells for single-cell RNA-sequencing
5. These were centrifuged at 1000rpm for 3 minutes at 4°C.
SECTION: Harvesting cells for single-cell RNA-sequencing
6. Cells were resuspended in 20μL chilled DPS.

SECTION: Harvesting cells for single-cell RNA-sequencing
7. 180μL chilled 100% methanol was added dropwise to the cells while gently vortexing to prevent cell clumps, before fixing the cells on ice for 15 minutes. 
SECTION: Single-cell RNA-sequencing protocol
8. Between 2400 to 4000 cells were loaded for each sample into a separate channel of a Chromium Chip G for use in the 10X Chromium Controller (cat: PN-1000120). 
SECTION: Single-cell RNA-sequencing protocol
9. The cells were partitioned into nanoliter scale Gel Beads in emulsions (GEMs) and lysed using the 10x Genomics Single Cell 3′ Chip V3.1 GEM, Library and Gel Bead Kit (cat: PN-1000121). 

SECTION: Single-cell RNA-sequencing protocol
10. cDNA synthesis and library construction were performed as per the manufacturer’s instructions.
SECTION: Single-cell RNA-sequencing protocol
11. The RNA was reversed transcribed and amplified using 12 cycles of PCR.
SECTION: Single-cell RNA-sequencing protocol
12. Libraries were prepared from 10µL of the cDNA and 13 cycles of amplification. Each library was prepared using Single Index Kit T Set A (cat: PN-1000213) and sequenced on the HiSeq4000 system (Illumina) using 100 bp paired-end run at a depth of 20-50 million reads. Libraries were generated in independent runs for the different samples.
SECTION: Single-cell RNA-sequencing data processing
13. Reads were aligned to the human reference genome (Ensembl release 93, GRCh38) using Cell Ranger v3.0.2.
SECTION: Single-cell RNA-sequencing data processing
14. The analysis was carried out using Seurat v3.0 (REF- Butler, 2018 ,Stuart, 2019 #645) following Seurat’s standard workflow. 
SECTION: Single-cell RNA-sequencing data processing
15. Cells expressing fewer than 200 genes were excluded from the subsequent analysis. In addition, we excluded cells with more than 3000 detected genes to remove suspected cell doublets or multiplets.
SECTION: Single-cell RNA-sequencing data processing
16. Due to the nature of the cells used for this experiment, a  10% cut-off was applied for the percentage of mitochondrial genes expressed to filter out likely apoptotic cells.
SECTION: Single-cell RNA-sequencing data processing
17. Using default parameters of Seurat, data for each sample were log normalised across cells and the 2000 most highly variable genes identified.
SECTION: Single-cell RNA-sequencing data processing
18. Using the canonical correlation analysis (‘CCA’) to identify anchors, we integrated the samples using Seurat v 3 (Butler, 2018; Stuart, 2019), followed by regression of the effect of cell cycle and scaling of the data. Dimensional reduction was performed using 50 PCs.
SECTION: Single-cell RNA-sequencing data processing
19. Clustree v0.4.4 and Seurat's plots functions were used to visualise the expression of astrocytic and neuronal marker genes across different cluster resolutions.
SECTION: Single-cell RNA-sequencing data processing
20. A clustering resolution of 0.25 was selected.
SECTION: Single-cell RNA-sequencing data processing
21. The differentially expressed genes between the clusters of interest were identified using Seurat's FindMarkers() and the default 'Wilcox' test.