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Sample GSM5029917

Query DataSets for GSM5029917

Status

Public on Jan 31, 2021

Title

2-microO

Sample type

SRA

Source name

Microglia 24M WT

Organism

Mus musculus

Characteristics

tissue: Cortex

Extracted molecule

total RNA

Extraction protocol

5,000 microglia from each population (including XO4+ and XO4- microglia, 6m, 9m, 12m and 24m CX3CR1GFP microglia) were sorted into DMEM/F12 media (supplemented with 5% (v/v) FBS, 50 U/ml Penicillin and 50 µg/ml Streptomycin), centrifuged at 12,000 xg for 2 min at 4°C and resuspended in 35μl of PBS containing 0.04% (w/v) BSA (0.22 µm filtered). The samples were then diluted with nuclease-free water in accordance to 10X single cell protocol guidelines to achieve a target cell recovery of approximately 800 cells/sample (for 6m WT, 24m WT, 6m XO4- and 6m XO4+ dataset), and target recovery of 10,000 cells per sample (age dataset 6m, 9m, 12m WT microglia). Single cell capture, RNA-seq library construction and sequencing were carried out at Micromon, Monash University using the 10X Genomics Chromium system (10X Genomics). Library construction was performed by poly-A selection from total RNA using 10X Chromium controller with Chromium Single Cell 3’ Reagent Kit V2 (10X Genomics, #PN-120237). Sequencing was performed on one High-Output lane of an Illumina NextSeq 550 (Illumina, California, USA) in paired-read 150 bp format. Chromium barcodes were used for demultiplexing and FASTQ files were generated using the Cellranger mkfastq pipeline. Alignment, filtering and UMI counting were performed using cellranger count. To improve detection of microglia, due to their low RNA content, cellranger reanalyse was used with the --force-cells option set at the inflection point when number of barcodes is plotted against the number of UMIs. Cells were manually filtered such that barcodes containing at least 10 counts corresponding to Cx3cr1, P2ry12 or Fcrls genes were classified as microglia, resulting in a total of 991 cells from the 4 FACS-sorted microglial populations.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

NextSeq 550

Description

10x Single Cell

Data processing

Single-Cell RNA Dataset : Chromium barcodes were used for demultiplexing and FASTQ files were generated using the Cellranger(Zheng et al. 2017) mkfastq pipeline. Alignment, filtering and UMI counting were performed using cellranger count. To improve detection of microglia, due to their low RNA content, cellranger reanalyse was used with the --force-cells option set at the inflection point when number of barcodes is plotted against the number of UMIs. Cells were manually filtered such that barcodes containing at least 10 counts corresponding to Cx3cr1, P2ry12 or Fcrls genes were classified as microglia, resulting in a total of 991 cells from the 4 FACS-sorted microglial populations.
Bulk RNA Dataset : Sequencing reads for the murine microglia dataset were sample demultiplexed with Je demultiplex from the JE suite using sequence barcodes in Supplementary table 11. Short sequence unique molecular identifiers (UMIs) from read pair 1 of the demultiplexed sample sequencing reads were discarded from both sequencing read pairs with Prinseq (minimum length 9). Remaining UMIs were clipped with Je clip and added to the sequencing read header to allow UMI deduplication post read mapping. Demultiplexed UMI tagged sequencing reads were filter-trimmed with Trimmomatic and aligned to the mouse genome (GENCODE's GRCm38 primary assembly annotation version vM15) using STAR(only sequencing reads from pair 2 were used for transcript quantification). Read deduplication based on UMIs was performed with Je MarkDupes and transcript read counts calculated with featureCounts. For the in vitro bulk sequencing dataset, demultiplexing was performed as we recently described (Grubman, A. et al. A CX3CR1 Reporter hESC Line Facilitates Integrative Analysis of In-Vitro-Derived Microglia and Improved Microglia Identity upon Neuron-Glia Co-culture. Stem Cell Reports (2020) doi:10.1016/j.stemcr.2020.04.007.) . In short, we used in-house pipelines including a fork of sabre tools (https://github.com/serine/sabre), and demultiplexed UMI-tagged sequencing reads were aligned to the human genome (Ensembl GRCh38 primary assembly) using RNAsik
Genome_build: GRCm38

Submission date

Jan 22, 2021

Last update date

Jan 31, 2021

Contact name

Gabriel Chew

E-mail(s)

[email protected]

Organization name

Duke-NUS Medical School

Street address

8 College Road