5 Drool Worthy Imaging Advances Of 2020
2020 was a difficult year for many, with their own research being interrupted- either by lab shutdowns or recruitment into the race against COVID-19. Despite the challenges, scientists have continued to be creative and have pushed the boundaries of what is possible. These are the techniques and technologies that every microscopist was envious of in 2020.
Spatially Resolved Transcriptomics
Nature Methods declared that spatially resolved transcriptomics was the 2020 method of the year. These are a group of methods that combine gene expression with their physical location. Single-cell RNA sequencing (scRNAseq) was originally developed for cells that had been dissociated from the tissues, so the location of the cell and how it was associated with neighboring cells were lost. Biologists have realized that context is an essential part of the puzzle needed for answering fundamental questions about development and disease.
Spatially resolved transcriptomics is not new, it has been around for a decade, but advances in imaging have made it a very powerful tool. Previously, there were problems with molecular crowding, photobleaching, and slow acquisition but expansion microscopy, brighter dyes, and rapid imaging techniques such as lightsheet microscopy have all overcome these problems.
Nanobodies are also known as single-domain antibodies; 10-times smaller than the conventional IgG antibodies. Nanobodies were first discovered in the 1980s, so again the technology isn’t new, but the way it is being used changed drastically in the last year.
Commercial companies recently started carrying nanobodies, and there are several advantages for microscopy. The first and most obvious is that nanobodies are only 2nm, which increases resolution (through less epitope displacement) when working with super-resolution technologies. Less obvious, but nonetheless important is that the small size allows users to more densely label their samples allowing a higher signal from your protein of interest.
There are other novel uses of nanobodies that have been described in 2020. One novel use of nanobodies is “ligand-modulated antibody fragments” (LAMAs) that can be chemogenetically controlled. This is a useful way to reversibly study the role of GFP-fusion proteins in biological processes. Another novel use that was published this year is the use of nanobodies embedded in organelle membranes to study how actin interacts with specific organelles. There will likely be quite a few other new uses for nanobodies that are published in 2021.
I first learned of MINFLUX when Stefan Hell gave a keynote lecture at the “Seeing is Believing” conference. He presented different ways that the technology could be used to achieve 1-3 nm resolution. At the end of the talk, he stated that he had started super-resolution microscopy and with the advent of MINFLUX he was closing the book on super-resolution hardware. Any more improvements in resolution would have to be achieved through this software. That seems like quite a bold statement, but only time will tell if he is correct. Currently, only one biologically relevant sample has been imaged in nuclear pore complexes. If MINFLUX can be applied to viral assembly, chromatin dynamics, or phase-phase separation it could be a powerful tool for discovery.
Anything that is electronic seems to be coming out with a “smart” version these days and microscopes are not the exception. The first smart features were detecting a sample and imaging just the sample (i.e. tissue section), but improvements are rapidly published and integrated into commercial equipment. The strength of smart microscopy is that data from acquisition give real-time feedback to change the parameters of the microscope. Rita Strack, senior editor at Nature Methods, suggests that the shift to smarter microscopy is “poised to eventually take humans out of the loop in imaging experiments.” Currently, there are methods to control illumination, spatial resolution, or regions of interest.
There is still plenty of room for growth in the acquisition side of microscopy, providing clearer pictures, less phototoxicity, and high-resolution imaging only of areas of interest.
GPU Image Reconstruction And Analysis
Artificial intelligence-based image reconstruction and analysis has been previously reserved only for groups with high powered computers and data scientists. This was generally due to the difficulty training the system and the dependence on massive CPUs. Then, about 5 years ago, more of the workload was transferred to GPUs, which are much quicker, but at the time this was only still done by well-funded labs.
At the end of 2019, a paper by Robert Haase introduced features for FIJI called CLIJ that allowed for GPU processing on any computer, including low-cost laptops. A laptop GPU could now process images faster than a workstation CPU. Some processing that would take days were cut down to mere hours on a workstation GPU.
Another powerful tool recently released is Noise2Void by the Jug group. It allows de-noising/restoration without the need to train the software on matched images of good vs poor images. Researchers may not have access to the super-resolution microscopes to be able to create matched pairs, but Noise2Void is able to improve the images without that requirement.
There is a whole alphabet soup of techniques that can assist with speeding up your analysis. Some examples are fcsSOFI for FCS imaging analysis, GIANI for 3D image sets, SRRF for live-cell super-resolution, and NanoJ-SQUIRREL for image quality estimation.
Technology in microscopy keeps pushing the limits and assists in key new biological discoveries. Hopefully, we continue to see rapid growth in 2021.
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