Single-cell and single-nucleus RNA sequencing can reveal cellular heterogeneity across tissues, immune environments and disease models. However, interpretable results depend on sample quality, workflow choice, QC and analysis—not sequencing depth alone.

This technical note outlines the project journey from sample assessment and preparation through sequencing, bioinformatics and data delivery. Using representative human brain and cryopreserved PBMC datasets, it shows how scRNA-seq and snRNA-seq provide complementary cellular views and how UMAP, cell-type annotation and composition analysis support interpretation.

• How sample quality affects cell or nuclei recovery
• When to consider scRNA-seq or snRNA-seq
• Which QC metrics help assess data quality
• How clustering and cell-type annotation support interpretation
• How UMAP and composition analysis can compare samples