LAUSR

There is a rapid increase of evidence to address the importance of the interaction between single cells, drugs, and the response of single cells to therapies. Single-cell measurements were used to evaluate the DNA-damaging ability of the herbicide in freshly isolated human leukocytes (Villarini et al. 2000) or the ethoxyresorufin-Odeethylase activity of cytochrome P450 1A1 in singleliving cells with the microspectrofluorometric technique (Taira et al. 2007). The measurements of single-cell biology and sequencing are recently considered as an important approach to investigate molecular mechanisms of drug efficacy and resistances, discovery and development of therapeutic targets, and genealogic phenotypes of cells during disease progression (Chu et al. 2017; Wang 2016; Wang et al. 2017). Single-cell sequencing is an important measure to define intercellular heterogeneity, rare cell types, cell genealogies, somatic mosaicism, microbes, and disease evolution, including single-cell DNA genome sequencing, DNA methylome sequencing, and RNA sequencing. Of those, single-cell RNA sequencing (scRNA-seq) demonstrates transcriptomic cell-to-cell variation, new cell types, developmental processes, transcriptional stochasticity, transcriptome plasticity, and genome evolution (Wang 2015) (Fig. 1). The present article aims to highlight the optimization and application of scRNA-seq to understand the development of intercellular heterogeneity, the genealogy and evolution of cells, and key driven transcriptome networks in response to drug efficacy and toxicity. The experimental design and technical challenges are critical in the application of scRNA-seq (Kukurba and Montgomery 2015). A number of practical protocols have been developed and validated with a great variation of RNA sequencing sensitivity and accuracy. Ziegenhain et al. (2017) made a comprehensive comparison of scRNA-seq protocols and suggested that an informed choice among six prominent scRNA-seq methods, including CEL-seq2, Drop-seq, MARS-seq, SCRB-seq, Smart-seq, and Smart-seq2, based on scRNA-seq data from mouse embryonic stem cells. Svensson et al. (2017) evaluated the protocol sensitivity and accuracy of the published data sets as well as the study designs by comparing it with 15 other protocols computationally and 4 protocols experimentally for batch-matched cell populations. Using the spike-in standards and uniform data processing, they developed a flexible tool for counting the number of unique molecular identifiers (https://github.com/vals/umis/). Such a protocol makes it possible to perform scRNA-seq and to compare gene expression, novel transcripts, alternatively spliced genes, and allele-specific expression among numerous studies and performers. Of scRNA-seq preparation procedures, cryopreserved cells using 3′-end and full-length RNA preparation methods was found to generate the same transcriptional profiles as fresh cells do (Guillaumet-Adkins et al. 2017). Intercellular heterogeneity is a dominant element of intratumor heterogeneity, responsible for the development Cell Biol Toxicol DOI 10.1007/s10565-017-9404-y