Table 1.

Comparison of each lineage tracing method

	Pros	Cons	Requirement
Imaging-based lineage tracing	Completely retains spatial information; does not need complicated algorithm for analysis; potential for multiple timepoint tracing/retracing; applicable to various tissues	Limits scalability of traced progeny; variation in marking is limited; generation of new (mouse) lines may be time-consuming; not easily coupled with scRNA-seq	Inducible CreER lines in desired tissue; tissue processing (sectioning or clearing); 3D microscopy (confocal, lightsheet, intravital)
Genetic barcoding	Relatively easy to assign barcodes to each cell; high scalability; can be easily coupled with scRNA-seq	Limited targetable tissues; lack of spatial information; single timepoint tracing	Barcode library, delivery methods, implantation techniques; library preparation for NGS; computational reconstruction analysis
Polylox system	Relatively easy to assign barcodes to each cell; high scalability; applicable to various tissues; can be easily coupled with scRNA-seq	Only available in mouse, currently; single timepoint tracing	Various Cre lines; library preparation for NGS; computational reconstruction analysis
CRISPR/Cas9-induced scar-based lineage tracing	Relatively easy to assign genetic scars to each cell; available in various model organisms; high scalability; potential for multiple timepoint tracing; can be easily coupled with scRNA-seq	Off-target effects and multiple DSBs could result in genotoxicity	Integrating target sequences and gRNAs for target sites; induction of Cas9 endonuclease; library preparation for NGS; computational reconstruction analysis
Natural DNA scar-based lineage tracing	Can be applied to human patient samples; least artificial set-up because it does not need any molecular or genetic intervention	High costs; needs high computational power to distinguish between clones; unknown origin of progeny; may require clonal derivation to improve coverage	In vitro cultures to amplify single clones or laser dissection of tissues; library preparation for NGS; computational reconstruction analysis