Profiling of Single-Cell Transcriptomes

Complex biological systems are composed of multiple cell types whose transcriptional activity can vary due to differences in cell state, environmental stimulation or intrinsic programs. Conventional bulk analysis methods capture the average transcriptional programs of the cell population, thus missing the unique cellular signature of each single cell. In recent years, the development of single-cell RNA-sequencing (scRNA-seq) technologies has provided a powerful approach to dissect the cellular heterogeneity of complex biological systems. However, such approaches required specialized equipment or were costly. In this protocol, we describe an improved Smart-seq2-based method to profile the transcriptome of hundreds of single cells simultaneously, without utilizing commercial kits or requiring any specialized single-cell capture/library preparation tools. Moreover, we introduce a data analysis pipeline, named Automated Single-cell Analysis Pipeline (ASAP), that allows researchers without strong computational expertise to explore scRNA-seq data using a wide range of commonly used algorithms and sophisticated visualization tools.