Biological information provided by scRNA-seq analysis

Cell-type Annotation: One of the major advantages of scRNA-seq is its ability to identify a wide variety of cell populations, including rare cell types. This technique can reveal previously unknown cell populations and provide essential insights into how specific cell groups are involved in disease environments or drug treatments. However, cell group identification is a highly labor-intensive process. Instead of manually evaluating the expression of each core transcript, researchers can use algorithms like Seurat Label Transfer or SingleR to streamline the process. Due to the sensitivity of scRNA-seq, there is a risk of false positives, so it is crucial to take a conservative approach when identifying new cell types. Accurate annotation must be grounded in previously established data to minimize misleading results.

Cellular Origin Analysis: Since scRNA-seq captures transcriptomic changes at specific time points, it has limitations in illustrating cellular dynamics over time. To address this, pseudo-time analysis is used. This technique arranges cells along a virtual timeline, enabling the reconstruction of cellular progression, from their origin to their final differentiation stage. For instance, it can trace the stages of CD8+ T cell differentiation within tumor tissues. Common algorithms for pseudo-time analysis include Slingshot and Scorpius. As with cell-type identification, a cautious, evidence-based approach is critical to ensuring the accuracy of results from pseudo-time analysis.

Functional Clustering: Functional clustering techniques, such as the Gene Set Enrichment Analysis (GSEA) commonly used in bulk RNA-seq, are also applied in scRNA-seq. These methods assess transcript expression across multiple genes to identify key pathways involved in functional changes. Unlike bulk RNA-seq, scRNA-seq provides the advantage of pinpointing which specific cell populations show the greatest pathway alterations due to disease or drug treatment. Moreover, scRNA-seq enables predictions about cell-to-cell interactions by ranking functional changes across different cell types. This analysis can uncover new cell-cell interactions and their roles in disease and drug response.