Learning cell identities and (post-)transcriptional regulation using single-cell data

Michielsen, L.C.M.

The human body consists of many different cell types. Cell types can be defined by the genes expressed, and unique cell-type-specific transcriptional mechanisms control these expressions. Single nucleotide polymorphisms (SNPs) in the DNA can be associated with diseases, but approximately 95% fall in the non-coding region. Usually, it is unknown whether these variants are causal, and which gene and cell type they affect.
Advances in single-cell RNA-sequencing improved our understanding of heterogeneous tissues and led to the discovery of many new cell types. This new technology also presents computational challenges including consistent cell-type annotations. It is essential to annotate cells using classification instead of currently practiced clustering methods. To facilitate this transition, we benchmarked cell-type classification methods and developed computational methods to automatically build reference atlases using multiple already labeled single...Show moreThe human body consists of many different cell types. Cell types can be defined by the genes expressed, and unique cell-type-specific transcriptional mechanisms control these expressions. Single nucleotide polymorphisms (SNPs) in the DNA can be associated with diseases, but approximately 95% fall in the non-coding region. Usually, it is unknown whether these variants are causal, and which gene and cell type they affect.
Advances in single-cell RNA-sequencing improved our understanding of heterogeneous tissues and led to the discovery of many new cell types. This new technology also presents computational challenges including consistent cell-type annotations. It is essential to annotate cells using classification instead of currently practiced clustering methods. To facilitate this transition, we benchmarked cell-type classification methods and developed computational methods to automatically build reference atlases using multiple already labeled single-cell datasets.
Next, we establish a relationship between mutations and their effect on gene or isoform expression. We study sequence-to-expression models that can predict an alteration in expression when a mutation is observed. Given that gene expression mechanisms are cell-type specific, we introduce sequence-to-expression models based on single-cell data to make cell-type-specific predictions. We use these models to show that certain mutations are indeed changing expression, increasing our understanding of transcriptional regulation.Show less

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Learning cell identities and (post-)transcriptional regulation using single-cell data

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