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A Spatial Point Pattern Analysis in Drosophila Blastoderm Embryos Evaluating the Potential Inheritance of Transcriptional States

Overview of attention for article published in PLOS ONE, April 2013
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Title
A Spatial Point Pattern Analysis in Drosophila Blastoderm Embryos Evaluating the Potential Inheritance of Transcriptional States
Published in
PLOS ONE, April 2013
DOI 10.1371/journal.pone.0060876
Pubmed ID
Authors

Feng He, Jun Ma

Abstract

The Drosophila blastoderm embryo undergoes rapid cycles of nuclear division. This poses a challenge to genes that need to reliably sense the concentrations of morphogen molecules to form desired expression patterns. Here we investigate whether the transcriptional state of hunchback (hb), a target gene directly activated by the morphogenetic protein Bicoid (Bcd), exhibits properties indicative of inheritance between mitotic cycles. To achieve this, we build a dataset of hb transcriptional states at the resolution of individual nuclei in embryos at early cycle 14. We perform a spatial point pattern (SPP) analysis to evaluate the spatial relationships among the nuclei that have distinct numbers of hb gene copies undergoing active transcription in snapshots of embryos. Our statistical tests and simulation studies reveal properties of dispersed clustering for nuclei with both or neither copies of hb undergoing active transcription. Modeling of nuclear lineages from cycle 11 to cycle 14 suggests that these two types of nuclei can achieve spatial clustering when, and only when, the transcriptional states are allowed to propagate between mitotic cycles. Our results are consistent with the possibility where the positional information encoded by the Bcd morphogen gradient may not need to be decoded de novo at all mitotic cycles in the Drosophila blastoderm embryo.

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The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 7%
Unknown 14 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 33%
Researcher 4 27%
Professor > Associate Professor 2 13%
Other 1 7%
Student > Doctoral Student 1 7%
Other 0 0%
Unknown 2 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 6 40%
Biochemistry, Genetics and Molecular Biology 4 27%
Computer Science 1 7%
Neuroscience 1 7%
Engineering 1 7%
Other 0 0%
Unknown 2 13%