In vivo lineage tracing across human tissues using methylation barcodes in the protocadherin gene cluster
Hackett, S. F.; Boniface, C. T.; Fonseca, A. V. A.; Ramos-Yamasaki, A. D.; Watson, C.; Bazin, H. M. L.; Tan, A. B.; Lee Yu, H.; Hanssen, L. L. P.; Dev et al.
Resolving the lineage history of human cells is fundamental to understanding ageing and cancer but remains hampered by a lack of native, high-resolution markers. Here, we identify the protocadherin (PCDH) gene cluster as a naturally occurring, highly diverse methylation barcode. While PCDH methylation creates neuronal diversity in the brain, we show that stochastic methylation patterns in this region are maintained as heritable, evolvable lineage markers across multiple non-neuronal tissues, including blood, kidney, prostate, and bladder. By tracking these barcodes in serial samples over a decade, we reveal clonal dynamics with high fidelity, quantitatively recapitulating genetic clone sizes. Crucially, PCDH barcodes identify "cryptic" clonal expansions invisible to standard driver-mutation sequencing and resolve subclonal architectures via continuous epimutation. This native barcoding system provides a scalable, driver-agnostic framework for reconstructing somatic evolution in humans.
Hijacking the brain's quirky protocadherin methylation for a full-body barcode bash, this method tracks clonal expansions like a sneaky cellular detective, spotting hidden growths in blood and organs that traditional mutation sleuths miss with epimutation flair.
Detailed in a thread by cancer researcher Jamie Blundell (@jrblundell), emphasizing its applications in tracing somatic evolution, with excitement over the field's pace from Alejo Rodriguez-Fraticelli (@AlejoFraticelli) and congrats from Dr. Bishoy M. Faltas (@FaltasLab)
View discussion on XPeer Reviews
Peer review in progress...
Your Assessment
Rate This Paper
Quick Takes
0 takesLoading...
More to Read
View All →CD4⁺ T cells confer transplantable rejuvenation via Rivers of telomeres
Lanna, A.; Valvo, S.; Dustin, M.; Rinaldi, F.
Using a GPT-5-driven autonomous lab to optimize the cost and titer of cell-free protein synthesis
Smith, A. A.; Wong, E. L.; Donovan, R. C.; Chapman, B. A.; Harry, R.; Tirandazi, P.; Kanigowska, P.; Gendreau, E. A.; Dahl, R. H.; Jastrzebski, M.; Cortez, J. E.; Bremner, C. J.; Hemuda, J. C. M.; Dooner, J.; Graves, I.; Karandikar, R.; Lionetti, C.; Christopher, K.; Consiglio, A. L.; Tran, A.; McCusker, W.; Nguyen, D. X.; Nunes da Silva, I. B.; Bautista-Ayala, A. R.; McNerney, M. P.; Atkins, S.; McDuffie, M.; Serber, W.; Barber, B. P.; Thanongsinh, T.; Nesson, A.; Lama, B.; Nichols, B.; LaFrance, C.; Nyima, T.; Byrn, A.; Thornhill, R.; Cai, B.; Ayala-Valdez, L.; Wong, A.; Che, A. J.; Thavaraj
A Single-Cell and Spatial 3D Multi-omic Atlas of Developing Human Basal Ganglia and Inhibitory Neurons
Heffel, M. G.; Xu, H.; Pastor-Alonso, O.; Li, X.; Baig, M. S.; Irfan Ghoor, R.; Li, R.; Kern, C.; Kum, J.; Zhang, Y.; Paino, J.; Tsai, M. J.; Tai, C.-Y.; Tucker, G.; Zhao, Z.; Hou, A.; von Behren, Z.; Bhade, M.; Li, S.; Sandoval, K.; Scholes, J.; Codrea, F.; Calimlim, J.; Liao, E. K.; Leung, G.; Kim, J.; Eskin, E.; Flint, J.; Cotter, J. A.; Pasaniuc, B.; Bintu, B.; Zhu, Q.; Mukamel, E. A.; Ernst, J.; Paredes, M. F.; Luo, C.
Prediction of transformative breakthroughs in biomedical research
Davis, M. T.; Busse, B. L.; Arabi, S.; Meyer, P.; Hoppe, T. A.; Meseroll, R. A.; Hutchins, B. I.; Willis, K. A.; Santangelo, G. M.