CellTagging: combinatorial indexing to simultaneously map lineage and identity at single-cell resolution.

Area of Science:

• Single-cell biology
• Genomics
• Molecular biology

Background:

• Single-cell technologies provide deep biological insights but often lose lineage information.
• Bulk analyses mask the behavior of rare cell populations.
• Understanding cell lineage is crucial for developmental biology and disease research.

Purpose of the Study:

• To present a detailed protocol for CellTagging, a method for simultaneous lineage and identity profiling at single-cell resolution.
• To enable the construction of multi-level lineage trees using high-throughput single-cell RNA sequencing.
• To provide a robust approach applicable across diverse biological systems.

Main Methods:

• Generation of complex plasmid and lentivirus CellTag libraries.
• Sequential CellTagging of cells during biological processes.
• High-throughput droplet-based single-cell RNA sequencing.
• Generation of CellTag expression matrix, clone calling, and lineage reconstruction.

Main Results:

• Demonstration of CellTagging for parallel capture of lineage and cell identity.
• Successful integration with high-throughput single-cell RNA sequencing.
• Establishment of a protocol for constructing multi-level lineage trees.
• Validation of a lentiviral-labeling approach for lineage and identity profiling.

Conclusions:

• CellTagging overcomes the loss of lineage information in single-cell studies.
• The protocol facilitates simultaneous profiling of lineage and identity at single-cell resolution.
• This lentiviral-labeling method is broadly applicable to various organisms and in vitro systems.