Single-cell transcriptome analysis is a very promising, yet a challenging method, for understanding various developmental and pathological processes in which fate is dictated by individual cells. RNA amplification from picogram amounts to quantities sufficient for next generation sequencing and/or microarray analysis, unavoidably introduces biases, that distort the single-cell transcriptome profile. In order to eliminate these biases, both experimental optimization of the amplification methodology and computational approaches are required. The latter comprehends normalization and scaling procedures aiming to correct the introduced biases and extract true biological traits. In this work, we compare microarray transcriptome profiling data from single MCF7 breast cancer cells with those from bulk (unamplified) RNA preparations from the same source, in order to evaluate, characterize and correct the biases introduced by the amplification protocols. Our approach involves scale-down measurements and the employment of normalization procedures, as well as internal normalization resources, such as housekeeping genes, to observe and minimize technical noise introduced by the experimental methods. Directing this work towards the understanding of single-cell processes responsible for the establishment of distant cancer metastasis we have further applied and validated our method on isolated individual circulating tumor cells (CTCs).
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- Towards Single-Cell Gene Expression Profiling: Assessing Amplification Biases