Single-cell sampling with RNA-seq analysis plays an important role in reference laboratory; cytogenomic diagnosis for specimens on glass-slides or rare cells in circulating blood for tumor and genetic diseases; measurement of sensitivity and specificity in tumor-tissue genomic analysis with mixed-cells; mechanism analysis of differentiation and proliferation of cancer stem cell for academic purpose. Our single- cell RNA-seq technique shows that fragments were 250–450？bp after fragmentation, amplification, and adapter addition. There were 11.6 million reads mapped in raw sequencing reads (19.6 million). The numbers of mapped genes, mapped transcripts, and mapped exons were 31,332, 41,210, and 85,786, respectively. All QC results demonstrated that RNA-seq techniques could be used for single-cell genomic performance. Analysis of the mapped genes showed that the number of genes mapped by RNA-seq (6767 genes) was much higher than that of differential display (288 libraries) among similar specimens which we have developed and published. The single-cell RNA-seq can detect gene splicing using different subtype TGF-beta analysis. The results from using Q-rtPCR tests demonstrated that sensitivity is 76% and specificity is 55% from single-cell RNA-seq technique with some gene expression missing (2/8 genes). However, it will be feasible to use RNA-seq techniques to contribute to genomic medicine at single-cell level. 1. Introduction Clinical specimens are tremendously different from biological specimens in that the former contain mixed cells while the latter are mostly composed of pure cells. A mixed cell population in clinical samples can mask real results of genomic data, resulting in an inaccuracy of routine clinical genomic analysis and clinical genomic diagnosis. However, genomic medicine requires precise genomic profiling of clinical specimens to work for a clinical genomic diagnosis and to design personalized therapy for genetic and cancerous diseases. Like most routine diagnosis techniques [1, 2], clinical genomic analysis and genomic diagnosis techniques also have two prerequisites, that is, sensitivity and specificity, for clinical analysis and diagnosis [3–5]. In order to meet the requirements, two techniques can be considered: quantitative real-time PCR (Q-rtPCR) [6] and single-cell genomic analysis. After clinical genomic data, such as microarray data, is analyzed, Q-rtPCR is employed to support the microarray results by using similar primer design in the PCR as microarray probes [7]. Although Q-rtPCR is often used to confirm genomic data analysis as a
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