We were delighted to host Dr. Kenta Imai, a system engineer from the Yokohama Nikon factory, on the 26th and 27th of August. He was interested in hearing about how our NIC users are using our systems and what they might want to see in new products. Additionally, as the manager of the STORM product line, Dr. Imai was able to help some of our users acquire some excellent STORM images.
To help our users we’ve now created a new page where we are storing the user guides for OMERO as well as for each scope system. So far, we have the Cyril guide prepared, and Sterling is on its way. These guides are helpful for post-training refreshers and as references for setting up systems for various imaging modalities. Use this LINK to see the guides.
A team from the Reck-Peterson Lab, in collaboration with Wade Harper’s lab at Harvard, recently published a paper in JCB showing that the dynein-dynactin activator protein Hook3 is capable of scaffolding KIF1C to dynein-dynactin http://jcb.rupress.org/content/early/2019/07/17/jcb.201812170.long. By forming a tripartite complex, KIF1C can use it’s plus-end directed motor activity to move the minus-end directed dynein-dynactin to the cell periphery, where it can then bind cargoes and move them in towards the cell center. The in vitro imaging data was produced on scopes in the Reck-Peterson lab, but they imaged their immunofluorescence samples on our A1R-HD confocal, and we were able to help them write an automated analysis script to rapidly quantitate co-localization between various forms of Hook3 and KIF1C.
Deconvolution is a standard technique in image processing for removing out of field data from fluorescence images (for a nice review see this iBiology lecture by David Agard). We have various deconvolution algorithms available on the workstations at the NIC, which can be confusing for users. So, I tested a variety of them on a standard specimen (a BPAE cell stained with Rhodamine-Phalloidin and an AlexaFluor488 labeled anti-tubulin antibody).
As you can see, the Landweber algorithm seemed to handle this sample well, and there wasn’t much difference between running the algorithm for 10 or 20 iterations (mainly differences in the chromatin). The Blind algorithm appears second best, and Richardson-Lucy and Automatic also fared well. The Fast method was the clear loser. Also, this cell was imaged with our C2 confocal, which is capable of producing such amazingly sharp images. We’re happy to show you how.
We now have a brand new SMZ800 dissecting scope in the center. It has 1x and 2x zoom lenses and will allow users to do dissections of Drosophila and C. elegans and mount specimens within the center. This scope is sitting in room 470, but can be moved to 471 if needed.
After some hardware and computer issues, Sterling, our combination Yokogawa CSU-X1 spinning disk, TIRF + STORM system is now up and running. If you are interested in this system, get in touch with us through the Contact Us page and we will be happy to discuss the system with you and book training sessions.
Users are frequently frustrated by trying to balance obtaining the right signal to noise ratio with photobleaching and phototoxicity when doing timelapse imaging. The introduction of singly-tagged fluorophores into endogenous loci increases this challenge. One solution for overcoming this challenge is to acquire images that are on the edge of usability (i.e. using the lowest possible laser power and short camera exposures) and then use post-processing steps to enhance the contrast and bring out the structural information that is detectable. In the image below from our spinning disk confocal, I have some neurons expressing a CRISPR GFP-tagged protein at endogenous levels that were imaged for two minutes with low laser power. These cells were also four weeks old at the time of imaging and not terribly healthy, adding to the degree of difficulty. To improve the original image (left), I first applied a local contrast filter before subtracting the background (middle image). Finally, I ran a 20 iteration blind deconvolution with the noise level set to “Grainy” to produce the image on the right. The resulting image, although non-quantitavive, has significantly higher contrast and moving particles are easy to detect during the timelapse. With NIS Elements it’s easy to write these steps into a GA3 recipe that can be applied in batch to all of the images from a given session.
We were happy to host Dr. Chieko Nakada and Ms. Chika Sakamoto from the Technology Solutions Sector of Nikon’s Yokohama Plant, and Dr. Jeff Bylund, Stem Cells and Regenerative Medicine Applications Manager at Nikon Instruments. Dr Nakada was eager to see our Center and to hear how our users are utilizing their instruments, the challenges that they face, and what they might like to see in future imaging systems.
On September 13th, the NIC had our official opening with talks from members of the UC San Diego administration and Nikon executives, a ribbon cutting, a reception and tours of the facility.