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 or the ethoxyresorufin-O-deethylase activity of cytochrome P450 1A1 in single-living cells with the microspectrofluorometric technique. 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. 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 (Fig. 1). Researchers at Fudan University 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.
Summary of processes, roles, dependent factors, and outcomes of
single-cell RNA sequencing (scRNA-seq)
Targeted cell populations are selected from human organs/tissues and then one type of cell population can be sorted. The selection of targeted cells is highly dependent upon the study design, goals, and cell purification a scRNA-seq of sorted cells can performed under the validated protocol, including measure systems, sample preparation, and methodologies. Intercellular heterogeneity identified can predict the sensitivity of targeted cells (green arrows) after proper data analyses, e.g., alignment qualification, quality control, normalization, and dimensionality. Drug-dependent gene mutations and drug specificity of targeted genes can be defined after selected cells are treated with targeting drugs (b, brown arrows). On the other hand, gene sequences and epigenetics of selected cells can be measured before the cell sorting (c, black arrows), in order to compare the cell population with single-cell sequences. Molecular mechanisms, drug efficacy and toxicity of identified core/driver genes and networks can be furthermore validated by editing target genes. One of the most dependent factors is how to interpret the correlation of information and outcome from scRNA-seq with cell heterogeneity, response, interaction, and phenotype as well as drug combination