Overview
This protocol describes a microfluidic system for the functional dissection of signaling oscillations in developing mouse embryos, specifically focusing on the segmentation of the presomitic mesoderm. The approach enables precise modulation and entrainment of endogenous signaling dynamics using external pulses of pathway modulators. The method is adaptable to various model systems and provides a platform for real-time imaging and analysis of signaling dynamics during embryonic development.
Key Study Components
Area of Science
- Developmental biology
- Microfluidics
- Embryology
Background
- Periodic segmentation in the presomitic mesoderm is regulated by dynamic signaling pathways.
- Oscillations and gradients in signaling control the timing and spacing of somite formation.
- Direct functional evidence for the role of signaling oscillations in somitogenesis has been limited.
- Microfluidics offers a means to modulate signaling dynamics with high precision.
Purpose of Study
- To establish a microfluidic protocol for modulating and entraining signaling oscillations in mouse embryonic tissue.
- To provide a detailed guide for first-time users to set up and utilize microfluidic systems in developmental biology research.
- To enable functional investigation of signaling dynamics and their impact on embryonic segmentation.
Methods Used
- Preparation of PDMS-based microfluidic chips using 3D-printed molds.
- Bonding of chips to glass slides via plasma treatment.
- Coating of chips with fibronectin and sterilization of components.
- Loading and culturing of primary mouse embryonic tissue within the microfluidic device.
- Entrainment of signaling oscillations using programmed pulses of pathway modulators.
- Real-time imaging and quantitative analysis of signaling dynamics.
Main Results
- Microfluidic entrainment successfully synchronized endogenous signaling oscillations in mouse embryonic tissue.
- Oscillation periods could be modulated to match the period of external drug pulses (e.g., 130 minutes).
- Phase relationships between oscillating pathways and external stimuli were quantitatively analyzed.
- The method demonstrated the importance of phase shifts between signaling pathways for proper segmentation.
Conclusions
- The microfluidic system enables precise functional analysis of dynamic signaling in multicellular systems.
- It provides a robust platform for studying the mechanisms underlying embryonic segmentation and signaling code.
- The approach is adaptable to other in vivo and in vitro models, such as organoids and gastruloids.
What is the main advantage of using microfluidics in studying signaling dynamics?
Microfluidics allows for precise, real-time modulation and synchronization of signaling oscillations without broadly affecting overall signaling activity, enabling detailed functional studies.
How is the microfluidic chip fabricated?
The chip is made from PDMS, mixed and cured in a mold created via 3D printing, then bonded to a glass slide using plasma treatment for a secure and sterile environment.
How are signaling oscillations entrained in the tissue?
Oscillations are entrained by applying programmed pulses of pathway modulators through the microfluidic system, synchronizing endogenous oscillations to the external stimulus.
What types of analyses can be performed with this system?
Researchers can perform real-time imaging of fluorescent reporters, immunostaining, in situ hybridization, and quantitative analysis of oscillation periods and phase relationships.
Can this protocol be adapted for other model systems?
Yes, the microfluidic approach is adaptable to other in vivo and in vitro systems, including organoids and gastruloids, for studying signaling dynamics and morphogen gradients.
What precautions are necessary to prevent issues during microfluidic experiments?
It is critical to degas the medium and chip, maintain high humidity to prevent evaporation, and avoid air bubbles, which can disrupt medium flow and tissue culture.
How is the success of oscillation entrainment confirmed?
Success is confirmed by aligning experimental data with drug pulse timing, visualizing with dyes, and quantitatively analyzing oscillation periods and phase relationships using tools like pyBOAT.