Diana R. O’Day

The paper develops STEAM, an evolution-augmented deep learning model, to predict mammalian enhancer landscapes across species using comparative genomics and single-cell data.

This study profiles 5.3 million cells from 55 rhesus macaques across their lifespan to map age-related changes in brain cell types and identify molecular signatures linked to aging and Alzheimer's disease.

The paper introduces Icebear, a neural network framework for cross-species imputation and comparison of single-cell transcriptomic profiles, revealing evolutionary adaptations of X-chromosome upregulation in mammals.

The paper uses single-nucleus ATAC-Seq and RNA-Seq to analyze age and sex differences in human heart cells, identifying gene expression and chromatin changes linked to these factors.

12.4 million single-nucleus transcriptomes from 83 staged mouse embryos (E8 to P0) yield 190 cell types, a zygote-to-pup cell-type tree, and rapid postnatal transcriptional shifts.

The paper creates a single-cell multi-omic atlas of the rhesus macaque brain, identifying cell-type-specific regulatory elements and providing insights into brain evolution and disease.

The paper establishes a single-cell atlas of human tooth development and uses it to guide the in vitro differentiation of ameloblasts from iPSCs, developing a mineralizing enamel organoid system.

This study maps the spatial and single-cell transcriptional landscape of human cerebellar development, revealing cell-type-specific gene expression and implications for neurological disorders.

The paper uses whole-genome and RNA sequencing from the human prefrontal cortex to identify genetic variants and transcriptomic changes associated with neurodevelopment and neuropsychiatric disorders.