What is correlative microscopy?
One of the major challenges for scientists is correlative microscopy — collecting and integrating data from multiple imaging techniques to better explore their samples.
How do you integrate this information? How do you make sure to precisely track regions of interest when moving between two imaging modalities using different resolutions?
This was the issue facing Dr. Matthia Karreman when she was a post-doctoral fellow at the Neurology Clinic and National Center for Tumor Diseases at the University of Heidelberg, Germany. Working in collaboration with the German Consortium for Translational Cancer Research and the German Cancer Research Center, Karreman and her team were studying how brain cancer cells spread in mice—specifically, how these tumor cells get carried out of blood vessels and into the brain.
Tracking regions of interest with in vivo imaging
To accomplish this goal, the research team captured in vivo images of brain tumor cells in live mice through small windows implanted into the skulls of their brains. Using intravital microscopy, the researchers were able to observe how the cancer progressed, but the resolution wasn’t high enough to observe events occurring at the subcellular level or how the tumor cells interacted with the surrounding tissue.
For this higher-resolution analysis, the team needed to examine the mice cancer cells using 3D electron microscopy. Yet finding the same cell with an electron microscope that had already been captured using in vivo imaging was a difficult process. “We needed to find one cell in the mouse brain and somehow narrow down the volume that we were looking at and find the proper position inside the sample,” says Karreman.
Overlaying datasets using Amira Software
To bridge these two imaging modalities, Karreman and her team used X-ray microCT imaging to capture an X-ray scan of the sample. The team then used Amira Software to build 3D models of the in vivo images showing the tumor cell and blood vessels with the X-ray images showing the blood vessels—correlating this data to find the common points in both datasets.
“Amira Software is able to overlay the in vivo dataset right on top of the X-ray dataset so I can know exactly where the tumor cell is,” Karreman says. “That allows me to very carefully remove all the brain material that I’m not interested in so I can expose the tumor cell for 3D electron microscopy.”
A powerful correlative microscopy solution
By correlating this data, Amira Software solved a problem that wasn’t possible using other solutions. “I had a dataset from in vivo imaging and needed some way to manipulate and move it to a dataset coming from the X-ray machine,” Karreman says. “The only software able to do that was Amira.”
Karreman believes the correlative microscopy workflow her team developed will have broad implications for other fields of biological research. Using the correlative microscopy features of Amira Software, scientists will easily be able to combine data from different imaging techniques. “I recommend Amira because it’s very powerful software that lets you do a lot with your images,” Karreman says. “It has certain unique tools that are not offered by other image process tools.”
To learn more, see Karreman’s correlative microscopy paper in the Journal of Cell Science. Also, visit our Amira Software webpage.
Victoria Limosino is a marketing program manager at Thermo Fischer Scientific.