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Sample GSM2898598 Query DataSets for GSM2898598
Status Public on Dec 23, 2017
Title MED_s260
Sample type SRA
 
Source name Single dendritic cell
Organism Homo sapiens
Characteristics cell type: DC
exposure: MED
exposuretime: 48H
donor: P2
platebatch: 24
viabilitysortbatch: yes
Extracted molecule total RNA
Extraction protocol Single cells lysates were put directly into SPRI
[Trombetta et al CPMB 2014]
[Single cell Whole Transcriptome Amplification]: Following sorting, 96-well plates of single cells were whole transcriptome amplified as described in Trombetta et al. Lysed cell samples were cleaned with 2.2x volume AMPure XP SPRI beads (Beckman Coulter). Reverse transcription and PCR were then performed on the samples. For population samples total RNA was isolated using a column (RNeasy plus Micro RNA kit; Qiagen) following manufacturer’s instructions. Two μL of extracted RNA were added to 8 μL of water and cleaned with 2.2x volume beads. While transcriptome amplification wa performed on these samples analogous to the amplification of the single cell samples.
[Preparation of cDNA Libraries for RNAseq]: WTA products were diluted to a concentration of 0.1 to 0.4 ng/μL and tagmented and amplified using Nextera XT DNA Sample preparation reagents (Illumina). Tagmentation was performed according to manufacturer’s instructions, modified to use ¼ the recommended volume of reagents, extended tagmentation time to 10 minutes and extended PCR time to 60s. PCR primers were ordered form Integrated DNA Technologies. Nextera products were then cleaned twice with at 0.9x volume of SPRI beads and eluted in water. The library was quantified using Qubit and analyzed using a high sensitivity DNA chip. The library was diluted to 2.2 pM and sequenced on a NextSeq 500 (Illumina)
Indexing length: 8bp
 
Library strategy RNA-Seq
Library source transcriptomic
Library selection cDNA
Instrument model Illumina NextSeq 500
 
Description norm_tpm.csv
s260
Data processing [Single-Cell RNA-Seq Expression Quantification] RNA-seq reads were aligned to the RefSeq hg38 transcriptome (GRCh38.2) using Bowtie2 (Langmead et al., 2012). The resulting transcriptome alignments were processed by RSEM to estimate the abundance (expected counts and TPM) of RefSeq transcripts (Li et al., 2011).
[Single-Cell Filtering and Gene Filtering] For each single-cell library we computed transcriptome alignment metrics using FastQC, Picard tools, and in-house scripts. Computed metrics included: (1) number of reads, (2) number of aligned reads, (3) percentage of aligned reads, (4) number of duplicate reads, (5) primer sequence contamination, (6) average insert size (7) variance of insert size, (8) sequence complexity, (9) percentage of unique reads (10) ribosomal read fraction, (11) coding read fraction, (12) UTR read fraction, (13) intronic read fraction, (14) intergenic read fraction, (15) mRNA read fraction, (16) coefficient of variation of coverage, (17) mean 5’ coverage bias, (18) mean 3’ coverage bias, and (19) mean 5’ to 3’ coverage bias.
We excluded from further analysis libraries with poor values for number of aligned reads (< 28,840), the percentage of aligned reads (< 15%), or the percentage of detected transcripts (< 33% of protein-coding genes expressed at >100 TPM in at least 10% of samples). Out of 2489 initial samples, only 393 samples passed.
Following cell filtering, genes were retained for downstream analysis if they were annotated as protein-coding, and expressed at levels greater than 100 TPM in at least 5 high-quality cells.
[Single-Cell Data Normalization] In order to normalize TPM data, we applied full-quantile normalization method, restoring original zero values to zero following normalization.
Genome_build: GRCh38.2
Supplementary_files_format_and_content: TPMs
 
Submission date Dec 22, 2017
Last update date Dec 27, 2017
Contact name Michael B Cole
E-mail(s) mbcole@berkeley.edu
Organization name UC Berkeley
Street address 378 Stanley Hall
City Berkeley
State/province CA
ZIP/Postal code 94720
Country USA
 
Platform ID GPL18573
Series (2)
GSE80212 A Reproducibility-Based Computational Framework Identifies An Inducible, Enhanced Antiviral Dendritic Cell State In HIV-1 Elite Controllers (scRNA-Seq)
GSE108445 A Reproducibility-Based Computational Framework Identifies An Inducible, Enhanced Antiviral Dendritic Cell State In HIV-1 Elite Controllers
Relations
BioSample SAMN08226721
SRA SRX3513229

Supplementary data files not provided
SRA Run SelectorHelp
Raw data are available in SRA
Processed data are available on Series record

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