Charting Single Cell Lineage Dynamics and Mutation Networks via Homing CRISPR
Wong, S. T.; Wang, L.; Dong, W.; Yin, Z.; Sheng, J.; Ezeana, C. F.; Yang, L.; Yu, X.; Wong, S. S.; Wan, Z.; Danforth, R. L.; Han, K.; Gao, D.
Show abstract
Single cell lineage tracing, essential for unraveling cellular dynamics in disease evolution is critical for developing targeted therapies. CRISPR-Cas9, known for inducing permanent and cumulative mutations, is a cornerstone in lineage tracing. The novel homing guide RNA (hgRNA) technology enhances this by enabling dynamic retargeting and facilitating ongoing genetic modifications. Charting these mutations, especially through successive hgRNA edits, poses a significant challenge. Our solution, LINEMAP, is a computational framework designed to trace and map these mutations with precision. LINEMAP meticulously discerns mutation alleles at single-cell resolution and maps their complex interrelationships through a mutation evolution network. By utilizing a Markov Process model, we can predict mutation transition probabilities, revealing potential mutational routes and pathways. Our reconstruction algorithm, anchored in the Markov models attributes, reconstructs cellular lineage pathways, shedding light on the cells evolutionary journey to the minutiae of single-cell division. Our findings reveal an intricate network of mutation evolution paired with a predictive Markov model, advancing our capability to reconstruct single-cell lineage via hgRNA. This has substantial implications for advancing our understanding of biological mechanisms and propelling medical research forward.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Modelling asymmetric count ratios in CRISPR screens to decrease experiment size and improve phenotype detection 95%
- Amplification-free long read sequencing reveals unforeseen CRISPR-Cas9 off-target activity 95%
- ZetaSuite, A Computational Method for Analyzing Multi-dimensional High-throughput Data, Reveals Genes with Opposite Roles in Cancer Dependency 95%
Similar papers in this journal
- Genome-wide functional screens enable the prediction of high activity CRISPR-Cas9 and -Cas12a guides in Yarrowia lipolytica 95%
- A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets 95%
- Biochemical-free enrichment or depletion of RNA classes in real-time during direct RNA sequencing with RISER 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.