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Pipeline publication

[…] rer’s recommendations including exogenous spike-in controls. cDNA was quantified using the Quant-iT PicoGreen dsDNA Assay kit (Thermo Fisher Scientific) and a high-sensitivity DNA chip (Agilent Technologies) and was diluted to a final concentration of 0.15–0.3 ng/µl. Diluted cDNA reaction products were used to generate scRNA-seq libraries using the Nextera XT DNA Sample Preparation kit (Illumina). After PCR amplification, all samples were pooled, purified, and quantified using a high-sensitivity DNA chip. Libraries were paired-end sequenced using a HiSeq 2500 sequencing system (Illumina). We sequenced 96 libraries per rapid-run flow cell, generating ∼15 × 106 reads per library. An index for RNA-seq by expectation maximization (RSEM) was generated on the basis of the Ensembl mm10 transcriptome downloaded from the University of California, Santa Cruz Genome Browser (23,637 total genes). Read data were aligned directly to this index using RSEM/bowtie. Quantification of gene expression levels in tags per million (TPM) for all genes in all samples was performed using RSEM (v1.2.8). Before all subsequent analyses, we filtered the data as previously described (). First, we filtered out cells with <2,500 genes with log2(TPM + 1) > 2. Second, we excluded genes that were log2(TPM + 1) < 4 in aggregated data. Third, we centered the data by subtracting from each gene its mean expression (log2[TPM + 1]) across all cells. After filtering, our dataset included 94 single cell transcriptomes, 54 representing 4-mo LMPPs and 40 representing 14-mo LMPPs, and 1,467 genes. These libraries had <20% of counts mapping to mitochondrial genes (4.64 ± 3.13%; mean ± SD). Principal component analysis was performed using the R stats prcomp() function with variables sc […]

Pipeline specifications

Software tools RSEM, Bowtie