Transmission electron cryomicroscopy
[2] The utility of transmission electron cryomicroscopy stems from the fact that it allows the observation of specimens that have not been stained or fixed in any way, showing them in their native environment.
This is in contrast to X-ray crystallography, which requires crystallizing the specimen, which can be difficult, and placing them in non-physiological environments, which can occasionally lead to functionally irrelevant conformational changes.
In a seminal paper in 1984, the group led by Jacques Dubochet at the European Molecular Biology Laboratory showed images of adenovirus embedded in a vitrified layer of water.
[8] This paper is generally considered to mark the origin of Cryo-EM, and the technique has been developed to the point of becoming routine at numerous laboratories throughout the world.
These low exposures require that the images of thousands or even millions of identical frozen molecules be selected, aligned, and averaged to obtain high-resolution maps, using specialized software.
[14] In 2017, the Nobel Prize in Chemistry was awarded jointly to Jacques Dubochet, Joachim Frank and Richard Henderson, "for developing cryo-electron microscopy for the high-resolution structure determination of biomolecules in solution".
Most biological specimens are extremely radiosensitive, so they must be imaged with low-dose techniques (usefully, the low temperature of transmission electron cryomicroscopy provides an additional protective factor against radiation damage).
For some biological systems it is possible to average images to increase the signal-to-noise ratio and retrieve high-resolution information about the specimen using the technique known as single particle analysis.
Three-dimensional reconstructions from CryoTEM images of protein complexes and viruses have been solved to sub-nanometer or near-atomic resolution, allowing new insights into the structure and biology of these large assemblies.
Besides all the benefits of high resolution images, the signal to noise ratio remains the main hurdle that prevents assigning orientation to each particle.
The good conditions for making the model that closely represent the real structure is when the data does not have too much noise and the particles do not have any preferential direction.
The main downside of maximum likelihood approach is that the result depends on the initial guess and model optimization can sometimes get stuck at local minimum.
Since the connection between the prior knowledge and the dataset is established, there is less chance for human factor errors which potentially increases the objectivity of image reconstruction.
