Applications and techniques of single-cell RNA sequencing across diverse species

Abstract

Single-cell ribonucleic acid sequencing (scRNA-seq) is an important tool in molecular biology, allowing transcriptomic profiling at the single-cell level. This transformative technology has provided unprecedented insights into cellular heterogeneity, lineage differentiation, and cell-type-specific gene expression patterns, significantly advancing our understanding of complex biological systems. scRNA-seq is broadly applied across various fields, including oncology, where it sheds light on intratumoral heterogeneity and precision medicine strategies, and developmental biology, where it uncovers cellular trajectories in both model and non-model organisms. Additionally, scRNA-seq has been instrumental in ecological genomics, which can help elucidate cellular responses to environmental perturbations and species interactions. Despite these advancements, several challenges remain, particularly technical and financial barriers, limiting its application to non-model organisms and tissues with complex cellular compositions. Addressing these issues will require continued innovation in single-cell isolation methods, cost-effective sequencing technologies, and sophisticated bioinformatics tools. As scRNA-seq advances, it can deepen our understanding of biological systems, with broad implications for personalized medicine, evolutionary biology, and ecological research.

Keywords: applications; cell differentiation; non-model organisms; single-cell RNA sequencing.

Figure 1

Distribution of bulk RNA-seq and scRNA-seq data according to taxonomy. (a) The pie charts show the distribution of RNA-seq data for bulk (left) and single-cell (right) analyses. Bulk RNA-seq data are dominated by animals (83.2%), with smaller proportions for plants (7.3%), microorganisms (6.7%), and fungi (2.8%). Single-cell RNA-seq data focus almost entirely on animals (98.3%), with minimal representation of other categories. (b) Bar plots show top taxa in bulk RNA-seq, with H. sapiens and M. musculus dominating animals, A. thaliana in plants, and S. cerevisiae and Escherichia coli in fungi and microorganisms. (c) Bar plots highlight single-cell RNA-seq taxa, mainly focusing on H. sapiens and M. musculus, with rare representation of A. thaliana, S. cerevisiae, and others.

Figure 2

Workflow for single-cell RNA-seq from sample preparation to downstream analysis across diverse species. BCL, base call file. GTF, gene transfer format. PCA, principal component analysis. KNN, k-nearest neighbors. UMAP, uniform manifold approximation and projection. t-SNE, t-distributed stochastic neighbor embedding. TF, transcription factor.