Table 2.

Advantages and limitations of RNA-Seq techniques for spatial mapping of biomarkers used in clinical oncology.

Type	Strength	Weaknesses	Suitable Applications
Bulk RNA-seq	Well-developed, cost-effective, and high throughput technique	Unable to determine spatial content; gene expression profiling is average	Whole transcriptome-based biomarker discovery, targeted RNA-seq panel for gene fusion
MERFISH	High-throughput, high-sensitivity, high-multiplex power	Reduced specificity and off-target binding	Spatial organization of the transcriptome inside the cells, 3D organization of the chromatin and chromosome, spatial atlases of cells in complex tissues
LCM-RNAseq	Performs cell-specific gene expression analysis	Low-quality data, time-consuming, unable to perform spatial profiling	Applied for tumor heterogeneity to the specific population of cells
Single-cell RNA-Seq	Capable to perform >10,000 single-cell gene expression analysis	Applicable to a limited number of unique transcripts, unable to reveal spatial content, high cost	Characterization and discovery of cell type tumor heterogeneity
Digital Spatial Profiling	Useful for FFPE materials, spatial profiling	Unable to reveal sequence information, restricted to a small number of gene panels only	Biomarker discovery, tumor microenvironments
Spatial transcriptomics	Spatial profiling, whole transcriptome analysis, sequence information	Time-consuming, the early phase of development	Tumor microenvironments, tumor heterogeneity
Fourth-generation RNA-seq	Potential of in situ sequencing	Not properly well developed	Great future potential but not demonstrated yet