Predicting cell population-specific gene expression from genomic sequence

Predicting cell population-specific gene expression from genomic sequence

Michielsen, L.C.M. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Mahfouz, A.M.E.T.A. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)

2024

Most regulatory elements, especially enhancer sequences, are cell population-specific. One could even argue that a distinct set of regulatory elements is what defines a cell population. However, discovering which non-coding regions of the DNA are essential in which context, and as a result, which genes are expressed, is a difficult task. Some computational models tackle this problem by predicting gene expression directly from the genomic sequence. These models are currently limited to predicting bulk measurements and mainly make tissue-specific predictions. Here, we present a model that leverages single-cell RNA-sequencing data to predict gene expression. We show that cell population-specific models outperform tissue-specific models, especially when the expression profile of a cell population and the corresponding tissue are dissimilar. Further, we show that our model can prioritize GWAS variants and learn motifs of transcription factor binding sites. We envision that our model can be useful for delineating cell population-specific regulatory elements.

sequence to prediction models
single-cell RNA-sequencing
gene expression prediction
transcriptional regulation
cell populations

http://resolver.tudelft.nl/uuid:75f56794-1226-4ead-bd79-ffb9d591b56d

https://doi.org/10.3389/fbinf.2024.1347276

2673-7647

Frontiers in Bioinformatics, 4

Institutional Repository

journal article

© 2024 L.C.M. Michielsen, M.J.T. Reinders, A.M.E.T.A. Mahfouz