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Figure 1 | Genome Biology

Figure 1

From: Single-cell analysis of lung adenocarcinoma cell lines reveals diverse expression patterns of individual cells invoked by a molecular target drug treatment

Figure 1

Generation of the RNA-Seq data from single cells of LC2/ad. (A) Read counts of spike-in controls. The tag counts corresponding to the indicated spike-ins are represented on the y-axis. The x-axis represents the copy numbers of the indicated spike-ins mixed in the sample. rpkm, reads per million tags per kilobase mRNA. (B) Complexity of the sequence reads. The number of RNA-Seq tags mapped to the same genomic position is shown. (C) Validation analysis using real-time PCR. Quantitative RT-PCR was conducted using first-strand cDNA for the genes listed in Additional file 3. Ct values were compared between the average of individual cells and those of the bulk of 200 cells. (D) Comparison between sequence duplicates (first panel), between biological duplicates (second panel) and between bulk and individual cells (third and fourth panels). The relation between gene expression levels measured from the average of independent cells and bulk RNA-Seq analysis of 200 cells (third panel) and >107 cells (fourth panel) are shown. Pearson’s correlation between two experiments is shown in the plot. (E) Identification of the fusion gene transcript, CCDC6-RET, using the RNA-Seq tags of single cells. The number of tags that directly spanned the junction point of the gene fusion is shown. In the upper panel, the densities of the RNA-Seq tags that were mapped to the indicated genomic positions (the RET gene region in the right half and the CCDC6 gene region in the left half) are also shown (in blue and red letters, respectively). The results in LC2/ad cells are shown. Note that even in the case where there was no RNA-Seq tag directly spanning the junction point, the distribution of the RNA-Seq tags were significantly different between the 5’ and 3’ halves of the RET gene, which indicates the discontinuity of this transcript.

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