ERK Signaling pp 337-351 | Cite as

Reconstructing ERK Signaling in the Drosophila Embryo from Fixed Images

Part of the Methods in Molecular Biology book series (MIMB, volume 1487)


The early Drosophila embryo provides unique opportunities for quantitative studies of ERK signaling. This system is characterized by simple anatomy, the ease of obtaining large numbers of staged embryos, and the availability of powerful tools for genetic manipulation of the ERK pathway. Here, we describe how these experimental advantages can be combined with recently developed microfluidic devices for high throughput imaging of ERK activation dynamics. We focus on the stage during the third hour of development, when ERK activation is essential for patterning of the future nerve cord. Our approach starts with an ensemble of fixed embryos stained with an antibody that recognizes the active, dually phosphorylated form of ERK. Each embryo in this ensemble provides a snapshot of the spatial and temporal pattern of ERK activation during development. We then quantitatively estimate the ages of fixed embryos using a model that links their morphology and developmental time. This model is learned based on live imaging of cellularization and gastrulation, two highly stereotyped morphogenetic processes at this stage of embryogenesis. Applying this approach, we can characterize ERK signaling at high spatial and temporal resolution. Our methodology can be readily extended to studies of ERK regulation and function in multiple mutant backgrounds, providing a versatile assay for quantitative studies of developmental ERK signaling.

Key words

ERK dynamics Quantitative imaging Drosophila embryo 



B.L. and S.Y.S. were supported by the National Institutes of Health Grant R01GM086537. C.J.D. was supported by the Department of Energy Computational Science Graduate Fellowship (CSGF), grant number DE-FG02-97ER25308, and the National Science Foundation Graduate Research Fellowship, Grant No. DGE 1148900. I.G.K. was supported by the National Science Foundation (CDS&E program through CBET and CMMI).


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Department of Chemical and Biological EngineeringPrinceton UniversityPrincetonUSA
  2. 2.Program in Applied and Computational MathematicsPrinceton UniversityPrincetonUSA
  3. 3.Lewis-Sigler Institute for Integrative GenomicsPrinceton UniversityPrincetonUSA

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