First Steps
This will help you to understand more about what we do at the B24 beamline. Please make sure you have completed the following before you join the workshop.
If you wish you can check out our brand new B24 playlist: beamline B24
- Read the published beamline papers (links included below)
- Briefly look over the assigned task but you are not expected to complete it before the workshop.
Downloading software (this will be necessary to complete the workshop assignment)
ImageJ is free software used for image analysis. It is used for viewing complex images such as 3D image volumes as well as extracting quantitative information from these images.
- Make yourself familiar with the different images in the dataset (links included below). Drag and drop them into Fiji to open them. (note: if you cannot open the images in Fiji you can watch the videos using any video player. Don’t worry if you don’t understand what they show yet, we will explain everything in the workshop)
Files provided:
The files here are representative of data collected at different times during an imaging experiment at B24 starting with:
At the structured illumination microscope:
- 3D brightfield data using visible light to scan the depth of an area of interest in our sample
- 3D raw structured illumination data using laser light to excite fluorophores in the sample
- 3D processed structured illumination data after in silico reconstruction to extract information beyond the diffraction limit (hence super-resolution)
At the soft X-ray transmission microscope:
- 2D mosaic X-ray image of the area of interest (remember that the higher the resolution the smaller the field of view we can capture)
- 3D raw X-ray tilt series using soft X-rays (500eV) to capture areas of high absorption (by cellular structures)
- 3D processed X-ray data (now called a tomogram) after in silico reconstruction to combine projections into a larger complete volume.
Homework
1. Using the reconstructed X-ray tomogram (file name: DataReconstructed-ReoSXT-Tomogram)
-Open the file with Fiji.
Q1. How many slices are there in this dataset?
Hint: A tomogram is a stack of images pulled together into a single volume.
Q2. Each pixel in this dataset is 16 nm in sample space. What is the total depth of the dataset in nm?
Hint: Set the scale of the image in Fiji by going to Analyze - Set scale.
Q3. What is the size of the field of view?
Hint: Use Image - Show Info in Fiji to look at the metadata in this file.
Q4. These data show the interface between two cells. At what depth do we see most of the vesicles in the cytoplasm?
Hint: we are looking for an approximate range in Z.
Q5. Approximately how many multivesicular bodies can we see in this tomogram?
Q6. Thinking about the resolution of our system (40 nm) and the contrast required to achieve this (3sigma at least) should we be able to identify single virions within vesicles in the cytoplasm?
Q7. The virus we are tracking here is reovirus, why can’t we see capsids inside the nucleus?
Hint: Think of the life cycle of this virus.
Q8. What is the approximate diameter of the biggest vesicle you can see?
Hint: use the line tool in Fiji to draw over the vesicle of your choice and multiply the length in pixels by the size of each pixel.
2. Using the reconstructed SIM data (file name: DataReconstructed-ReoSIM)
Q9. Open the SIM data in Fiji, go to Image - Colors - Channels tool and choose Color in the drop down menu. Change the first channel to Green and the second channel to Red and then change the View (using the drop down menu) to Composite. Some of the vesicles are now yellow. What does this mean?
3. Using the fully correlated data (file name: DataCorrelated-ReoCLXT)
Q10. Open the correlated video in Fiji. What more do we learn from the correlation of fluorescence and X-ray imaging data?
Hint 1: The green fluorescence is the virus and the red fluorescence is the chemical composition of the vesicles. In grayscale is the X-ray absorption of biological material (especially membrane bound organelles).
Hint 2: This is discussed in our beamline paper.
Biological Cryo-Imaging
