Building on the last post, let’s focus on the initial steps of the experimental workflow and how to transfer these concepts into practice. Your research aims are the foundation, and they will guide your choice of model and define the type of information needed from the experiment. The research aims and model choice are intertwined, and typically are set by your supervisor’s research program, unless you are tasked with exploring a new direction for your group. The other choices involve the imaging needs, and you will have to make decisions about the scale examined, including the volume and depth, targets, as well as timing for dynamic processes. These considerations are summarized in the accompanying visual guide.
After defining the model and information required from the imaging experiment, it’s time to evaluate the options for imaging modalities and sample preparation. Linking the research aims to the model and imaging requirements can be challenging for someone new to the field. If you are new to imaging and not sure what’s feasible, it’s a good idea to search the literature for similar experiments and to consult with your supervisor or another imaging expert in your laboratory. If you plan to access a shared imaging platform, you can reach out to the personnel as this will ensure that your experiment matches the technological capabilities of the instruments.
It is important to recognize that imaging, like any other technique, can require compromises. The optimal experiment often requires balancing different needs for spatial resolution, number of targets, and when examining dynamic processes, acquisition speed as well. For example, you may want to track calcium sparking at the microsecond scale in ten different types of immune cells as they migrate to the site of infection, yet with current technology, this is not practical. Instead, you would need to evaluate the research aims, and decide whether temporal resolution or imaging multiple targets is more important. If microsecond timing is most important, then you may have to study one or two immune cell types rather than ten. Also recall that optimizing the experimental design is iterative, and you may need to cycle through different approaches and conditions before finalizing your experimental conditions. Further, the information you initially think you need often changes as you visualize the model, as imaging may reveal new and unexpected insights about your biological question.
The table lists several representative imaging experiments drawn from our local research community. Each experiment required choices to align the research question to the model and imaging modality. Each experiment optimization, as the acquisition settings and sample preparation, were tested and refined. Like any skill, designing and optimizing experiments such as these requires knowledge, experience, and continual practice. In future posts, I’ll delve further into how to translate these concepts to your own research.
Linking the research question to experimental design: representative examples
Does perturbing a signaling pathway inhibit cell migration during wound healing?
Does treatment with a pharmacological compound inhibit mitochondrial integrity?
How does a protein mutation affect ion channel architecture and spatial distribution?
Can lung fibrosis be reversed using a novel therapeutic approach?
|Live epithelial cell line||Live human fibroblast cells||Fixed human brain tissue||Human biopsy samples|
Scale of organization
Volume and depth
|Single image of the entire population||3D volume of entire cell monolayer||Volume reaching from surface 0.6 microns into the tissue sample||150 microns from the surface of the sample|
Time (if applicable)
|Cells imaged every 10 minutes for 72 hours||1 image every 10 seconds for 15 min||Fixed cells, no time lapse needed||Fixed preparation, no time lapse needed|
|Phase contrast microscopy||Differential interference contrast and confocal fluorescence using one color||Fluorescence superresolution techniques using 2 colors||Multiphoton microscopy using second harmonic generation and one fluorescence using one color channel|