Single-Cell Transcriptomic Analysis of Human Primary Cells

Abstract

Single-cell transcriptomic analysis of primary cells enables the characterization of cellular heterogeneity that is averaged out in conventional RNA sequencing (RNA-seq), by capturing the transcriptomic profile of individual cells. Single-cell RNA-seq (scRNA-seq) has now become one of the fundamental analytical approaches in modern cell biology research. This article outlines the basic principles of scRNA-seq and its differences from conventional RNA-seq, and describes practical aspects of scRNA-seq analysis of tissue-derived primary cells, using human amniotic epithelial cells (hAECs) as an example. In droplet-based scRNA-seq, individual cells are labeled with identifiers that allow their cellular origin to be distinguished, enabling gene expression to be analyzed at the single-cell level. The resulting data are processed through quality control, normalization, dimensionality reduction, and clustering, followed by the identification of genes characteristically expressed in each cluster and cluster annotation to infer the properties of cell populations. When appropriate, sample integration and inference of continuous changes in cellular states can also be performed. However, in primary cells such as hAECs, enzymatic digestion, mechanical stress, sorting, and culture conditions may affect transcriptional profiles. Therefore, careful optimization of cell processing conditions is essential. scRNA-seq also has inherent limitations, including limited detection of lowly expressed genes, loss of spatial information, and difficulty in interpreting the biological meaning of cell clusters. To use scRNA-seq effectively, researchers need a clear biological question and a compelling rationale for single-cell-level analysis. With appropriate experimental design, collaboration with bioinformatics experts, and careful interpretation of the data, this approach can provide new perspectives and/or hypotheses for basic biomedical research.