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Sample GSM5029992

Query DataSets for GSM5029992

Status

Public on Jan 31, 2021

Title

E70_AGGCAGAA

Sample type

SRA

Source name

Microglia

Organism

Mus musculus

Characteristics

tissue: Microglia

Extracted molecule

total RNA

Extraction protocol

Libraries were made by individual first strand synthesis which add the index sequences indicated above.500pg of each total RNA sample was used as starting material except for a few samples where the entire RNA provided was used. An iPSC control was also processed with each set using a separate index. Following first strand synthesis cDNA of each set was pooled for subsequent processing.2.Final libraries were QC’d by Qubit, Bioanalyzer and qPCR.Thelibrary poolswere with mean size of ~350bp at a concentration of 4.8nM, 6.1nM, 4.8nM and 5.0nM respectively.3.For all four pools, following denaturation 2.2pM of library pool was clustered on a high output NextSeq runbased upon previous results with this type of sample.4.Clustering was within theoptimal rangefor all runsat 210k/mm2, 190k/mm2, 218k/mm2 and 190k/mm2 separably(optimal 170-230k/mm2). This resulted in 545.7million, 492.1million, 564.9million and 493.7million readable clustersaccordingly. Clusters passing filter wasas expected for NextSeq (significantly lower than HiSeq)with 81%, 82%, 71% and 86% (expected >70%). Percentage of reads >Q30 was also very good for all sets with Set1 94% >Q30 for R1 and71% >Q30 for R2; Set2 95% >Q30 for R1 and 66% >Q30 for R2; Set3 90% >Q30 for R1 and 70% >Q30 for R2; Set4 94% >Q30 for R1 and 73% >Q30 for R2(expected >80% for R1 and >60% for R2). This resulted in 442.8million, 405.6 million, 399.5 million and 423.6 millionfinal filtered reads respectively. 5.Library is designed so custom R1 primer reads directly into index followed by 10bp UMI. 6.R2 primer reads into RNA in the sense direction and will be best for transcript identification.7.Data is provided as 2matching Fastq files for R1 and R2.

Library strategy

RNA-Seq

Library source

transcriptomic

Library selection

cDNA

Instrument model

NextSeq 550

Description

Bulk-RNA Seq

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