Karin Isaev
RNA splicing as a source of cellular diversity
https://thetransmitter.org
Neurons express different genes at different times with major changes that depend on activity levels and patterns, hormones. Differences in cell state – cells of the same type expressing different genes under different conditions vs differences in cell types? Is there a terminal state?
- How do neural cell types diversify, differentiate and mature?
- Which cell types are evolutionarily conserved and which arise to meet the needs of particular species?
- What cell types are affected in neurological and psychiatric diseases?
Single-cell long-read sequencing-based mapping reveals specialized splicing patterns in developing and adult mouse and human brain. Jogelkar et. al.
sQTLs – list of splice junctions – look at their prevalence across specific cell types of interest to get a more granular view of their expression and potentially functional relevance.
Studying RBP regulation on single cell level – contribution to differences in cell identity and phenotype..
Using exon coverage to estimate splice junctions – Leafcutter ITI (Xingpei).
Datasets for deep learning models beyond SpliceAI + Pangolin (Tatsu).
Alternative Transcript Structure Event (ATSE).
\[ y ~ \mathrm{Binomial} (N, p) \]
Beta-Dirichlet factor model for splicing. Use variational inference, approximate log Bayes Factor (ALBF).
Data simulated from mouse brain single cell data. AnnData / simulation model.
Allen Brain Cell Atlas.
- Train on >110,000 single cells (Smart-seq2)
- Train on >70,000 single cells (Smart-seq2)
Mapping more sparse single cell data onto reference.