Written by John Heddleston
How is orientation of the Lattice Light Sheet Microscope objectives different?
As mentioned in an earlier post, the lattice light sheet microscope (LLSM) requires special consideration when preparing and mounting samples for imaging. This is due to the size of the water-dipping objectives (thereby restricting useable imaging volume) and the need to reduce potential refractive index changes between the excitation objective and the sample. However even after properly mounting a sample for imaging, the raw data produced by the microscope can be hard to intuitively understand due to the angle between the detection objective lens and the sample stage (Figure 1).
Figure 1: Objective Orientation
The excitation and detection objectives (black and silver, respectively) are mounted orthogonal to each other but at a 31.8°angle with respect to the Z (vertical) axis of the sample. To further complicate data interpretation, the sample is moved through the light sheet rather than the light sheet being scanned (in Z) through the sample, which results in the data being skewed (Figure 2A). Custom algorithms allows us to deskew the raw data into a format that is useable and intuitive (Figure 2B).
Figure 1: (A) Skewed and (B) Deskewed data
How does objective orientation affect data collection?
A better way to understand the orientation of the sample and objectives is via schematic in Figure 3. The figure demonstrates how trigonometric identities let us calculate the sample Z step (b) from the stage Z step (a).
What tools are available in the AIC for working with these data?
During a visit to Janelia, we use the local computer cluster to deskew and deconvolve all data generated by the lattice light sheet microscope. During data collection, the Labview software that operates the LLSM has a built-in utility that allows for quick deskewing to see data in a max intensity projection format and gives the user an idea how the data will look after deskewing on the AIC workstation. The goal is to give visitors their raw data, raw deskewed data, and deskewed deconvolved data prior to the end of the visit. This should give useful starting points for any data analysis or post-processing required. We have also written a Matlab-based deskew GUI that can be given to a visitor if s/he needs to deskew the data while at their home institution.