Abstract
Lineage tracing is a powerful tool to study cell history and cell dynamics during tissue development and homeostasis. An increasingly popular approach for lineage tracing is to generate high-frequent mutations at given genomic loci, which can serve as genetic barcodes to label different cell lineages. However, current lineage tracing mouse models suffer from low barcode diversity and limited single-cell lineage coverage. We recently developed the DARLIN mouse model by incorporating three barcoding arrays within defined genomic loci and combining Cas9 and terminal deoxynucleotidyl transferase (TdT) to improve editing diversity in each barcode array. We estimated that DARLIN generates 1018 distinct lineage barcodes in theory, and enables the recovery of lineage barcodes in over 70% of cells in single-cell assays. In addition, DARLIN can be induced with doxycycline to generate stable lineage barcodes across different tissues at a defined stage. Here we provide a step-by-step protocol on applying the DARLIN system for in vivo lineage tracing, including barcode induction, estimation of induction efficiency, barcode analysis with bulk and single-cell sequencing, and computational analysis. The execution time of this protocol is ~1 week for experimental data collection and ~1 d for running the computational analysis pipeline. To execute this protocol, one should be familiar with sequencing library generation and Linux operation. DARLIN opens the door to study the lineage relationships and the underlying molecular regulations across various tissues at physiological context.
Key points
The DARLIN mouse enables the study of the cell lineages of millions of cells and at a high efficiency in vivo.
Compared with other lineage-tracing mouse models, which can suffer from low barcode diversity and limited single-cell lineage coverage, the DARLIN mouse incorporates three barcoding arrays within defined genomic loci and combines Cas9 and terminal deoxynucleotidyl transferase to improve editing diversity in each barcode array.
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Fig. 1: DARLIN mouse barcoding system.
Fig. 2: Schematic of mouse line genetics and the generation of DARLIN.
Fig. 3: General considerations for lineage tracing experiments.
Fig. 4: Representative agarose gel image of edited DARLIN array.
Fig. 5: Representative TapeStation results of bulk DARLIN libraries.
Fig. 6: DARLIN data analysis.
Fig. 7: Clone identification from partial barcode measurements in the three genomic loci in DARLIN.
Fig. 8: A representative QC report from the Results.txt file after data processing.
Data availability
Raw and intermediate data associated with this tutorial can be obtained in https://zenodo.org/records/11929508.
Code availability
The snakemake_DARLIN package for DARLIN data processing is available at https://github.com/ShouWenWang-Lab/snakemake_DARLIN. The companion Python package for downstream analysis is available at https://github.com/ShouWenWang-Lab/MosaicLineage. A tutorial for downstream analyses written in jupyter notebooks can be found at https://github.com/ShouWenWang-Lab/DARLIN_tutorial.
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Acknowledgements
The authors would like to thank F. Garcia Osorio for his work on developing the Cas9–CARLIN model and initial barcode amplification protocols. L.L. would like to thank H. Wu, a former research assistant in her laboratory, for plotting the schematic genetics of DARLIN mouse. S.-W.W. would like to thank M. Chen, a PhD student in his laboratory, for formatting this manuscript. S.-W.W. acknowledges support from Westlake High-Performance Computing Center. L.L. is supported by National Key Research and Development Project of China (2024YFA1306603). L.L. and S.-W.W. acknowledge support from ‘Pioneer’ and ‘Leading Goose’ R&D Programs of Zhejiang province (2024SSYS0034 and 2024SSYS0034). S.B. acknowledges support from the NIH (grant number 1K99HL164969). S.-W.W. acknowledges support from the NSFC (32470700). F.D.C. is funded by grants R01HL128850, RC2DK131963 and R01HL158192, and an Alex Lemonade Crazy 8 award.
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Author notes
These authors contributed equally: Li Li, Sarah Bowling.
Authors and Affiliations
Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China
Li Li, Daolong Chen & Shou-Wen Wang
School of Life Sciences, Westlake University, Hangzhou, China
Li Li, Hongying Lin, Daolong Chen & Shou-Wen Wang
Department of Developmental Biology, Stanford University School of Medicine, Stanford, CA, USA
Sarah Bowling
School of Science, Westlake University, Hangzhou, China
Shou-Wen Wang
Stem Cell Program, Boston Children’s Hospital, Boston, MA, USA
Fernando D. Camargo
Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
Fernando D. Camargo
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Contributions
This protocol is based on a paper by L.L., S.B., S.-W.W. and F.D.C., where L.L. and S.B. developed the DARLIN model with input from F.D.C., and S.-W.W. developed and carried out computational analyses. Here, S.-W.W., L.L. and S.B. wrote the manuscript. H.L. helped to develop the analyses tutorial and generated Figs. 3 and 7 with supervision from S.-W.W.; and D.C. generated Figs. 2, 4 and 5 with supervision from L.L.
Corresponding authors
Correspondence to Shou-Wen Wang or Fernando D. Camargo.
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Key references
Li, L. et al. Cell 186, 5183–5199.e22 (2023): https://doi.org/10.1016/j.cell.2023.09.019
Bowling, S. et al. Cell 181, 1410–1422.e27 (2020): https://doi.org/10.1016/j.cell.2020.04.048
Patel, S. H. et al. Nature 606, 747–753 (2022): https://doi.org/10.1038/s41586-022-04804-z
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Li, L., Bowling, S., Lin, H. et al. DARLIN mouse for in vivo lineage tracing at high efficiency and clonal diversity. Nat Protoc (2025). https://doi.org/10.1038/s41596-025-01141-z
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Received:27 June 2024
Accepted:07 January 2025
Published:21 March 2025
DOI:https://doi.org/10.1038/s41596-025-01141-z
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