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E. Hilary Gustafson

Researcher at European Bioinformatics Institute

Publications -  11
Citations -  1773

E. Hilary Gustafson is an academic researcher from European Bioinformatics Institute. The author has contributed to research in topics: Enhancer & Transcription factor. The author has an hindex of 9, co-authored 11 publications receiving 1478 citations. Previous affiliations of E. Hilary Gustafson include Austrian Academy of Sciences & Rockefeller University.

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Guided self-organization and cortical plate formation in human brain organoids

TL;DR: Microfilament-engineered cerebral organoids (enCORs) model the distinctive radial organization of the cerebral cortex and allow for the study of neuronal migration and demonstrate that combining 3D cell culture with bioengineering can increase reproducibility and improve tissue architecture.
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Tissue-specific analysis of chromatin state identifies temporal signatures of enhancer activity during embryonic development.

TL;DR: This new approach to obtain cell type–specific information on chromatin state and RNA polymerase II (Pol II) occupancy within the multicellular Drosophila melanogaster embryo identifies dynamic enhancer usage, an essential step in deciphering developmental networks.
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A Transcription Factor Collective Defines Cardiac Cell Fate and Reflects Lineage History

TL;DR: It is demonstrated that the five genetic components essential for cardiac specification in Drosophila, including the effectors of Wg and Dpp signaling, act as a collective unit to cooperatively regulate heart enhancer activity, both in vivo and in–vitro.
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Shadow Enhancers Are Pervasive Features of Developmental Regulatory Networks

TL;DR: Embryogenesis is remarkably robust to segregating mutations and environmental variation; under a range of conditions, embryos of a given species develop into stereotypically patterned organisms and patterns of segregating variation suggest that they play a more complex role in development than generally considered.
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Model-based Method for Transcription Factor Target Identification with Limited Data

TL;DR: The approach is found to be comparable or superior to ranking based on mutant differential expression scores, and it is shown how integrating complementary wild-type spatial expression data can further improve target ranking performance.