In the early stages, scRNA-seq requires high cell viability and integrity (cell survival rate of over 80%), which poses challenges in cell sorting or isolating cells from tissues for sample preparation.

Additionally, handling large amounts of single-cell data may require addressing issues like dimensionality reduction, noise, low-expression genes, and confirming the biological interpretation of clustering results.

Traditional RNA-seq, known as Bulk RNA-seq, sequences and analyzes RNA from a large number of cells, providing the average gene expression profile of the overall cell population.

It is used to explore gene expression changes across entire tissues or cell populations, comparing expression differences under different conditions and identifying potential biological changes. 

On the other hand, scRNA-seq builds libraries for individual cells, obtaining gene expression information at the single-cell level, enabling the resolution of cellular heterogeneity and distinguishing between different cell types, subtypes, and states.

Overall, Bulk RNA-seq is more suitable for rapid analysis of large cell populations, while Single cell RNA-seq is better suited for revealing cellular heterogeneity and analyzing gene expression differences at the level of individual cells.

The choice of method depends on the research question and the biological problems that need to be addressed.