proteins give homogenous samples for atomic-resolution reconstruction. The fact
that proteins are dynamic leads to heterogeneity and underlies the need for large
amount of data collection (in a hope to group particles into homogenous groups),
which is tedious to be done manually. In recent years, many software packages
have been developed to interface with the advanced electron microscopes for
automatic data acquisition. Some examples of such software that can be used for
fully automated data collection on a well-calibrated cryo-TEM are Leginon [35],
SerialEM [36], UCSFImage4 [37], FEI-EPU, JEOL-JADAS [38], GATANLatitude S. Most of the software is used for automated data collection for both
single-particle cryo-EM and electron tomography (ET) work. Some programs like
Appion [39] extend the automated data collection through a pipeline from automated data collection all the way through automated particle picking to image
processing (CTF estimation, classification, and 3D reconstruction).
2.3 Image Processing and Three-Dimensional
Reconstruction
Cryo-EM is different from X-ray crystallography because it uses “images” as primary data, rather than the diffraction patterns. Translated into Fourier lingo, the
availability of images means that the “phase problem” known in X-ray crystallography (described in Sects. 2.3 and 2.4 of Natesh [3]) does not exist in EM. The
electron microscope, in Hoppe’s words, is a “phase-measuring diffractometer” [40].
Hence, extreme care has to be taken in image processing. Image processing
involves preprocessing the collected data, particle picking, centering the particles in
their selected boxes, 2D classification and determining their relative orientations
and/or 3D classification and 3D reconstruction. An example of image processing
and 3D reconstruction is shown in Figs. 3 and 4. The preprocessing step involves
CTF correction and image normalization [41]. As mentioned in the data collection
section, the data is collected at various defocus positions. As one gradually
increases the defocus (i.e., under focus), the contrast of the image proportionally
improves. Improvement in contrast comes at a cost, a loss in the higher spatial
frequencies (i.e., high-resolution information is lost) in the image, and in addition, it
introduces CTF modulation in the spatial frequencies of the image. Hence, the first
step in image processing is to calculate the lens defocus and astigmatism, which is
needed to correct the measured data for the CTF of the microscope [42, 43].
Software CTFFIND, ACE2, Gctf, or e2ctf.py [44–47] can be used to estimate the
CTF that is used for CTF corrections.
After CTF correction, the images are normalized to set the mean density of the
particles to zero and same standard deviation [41]. The particles are then manually
or auto-picked into boxes of 1.5–2.5x, the size of the largest axis of the particle
using suitable software. A guide for choosing the right box size is given at the
online documentation http://blake.bcm.edu/emanwiki/EMAN2/BoxSize. Number
384
R. Natesh
that proteins are dynamic leads to heterogeneity and underlies the need for large
amount of data collection (in a hope to group particles into homogenous groups),
which is tedious to be done manually. In recent years, many software packages
have been developed to interface with the advanced electron microscopes for
automatic data acquisition. Some examples of such software that can be used for
fully automated data collection on a well-calibrated cryo-TEM are Leginon [35],
SerialEM [36], UCSFImage4 [37], FEI-EPU, JEOL-JADAS [38], GATANLatitude S. Most of the software is used for automated data collection for both
single-particle cryo-EM and electron tomography (ET) work. Some programs like
Appion [39] extend the automated data collection through a pipeline from automated data collection all the way through automated particle picking to image
processing (CTF estimation, classification, and 3D reconstruction).
2.3 Image Processing and Three-Dimensional
Reconstruction
Cryo-EM is different from X-ray crystallography because it uses “images” as primary data, rather than the diffraction patterns. Translated into Fourier lingo, the
availability of images means that the “phase problem” known in X-ray crystallography (described in Sects. 2.3 and 2.4 of Natesh [3]) does not exist in EM. The
electron microscope, in Hoppe’s words, is a “phase-measuring diffractometer” [40].
Hence, extreme care has to be taken in image processing. Image processing
involves preprocessing the collected data, particle picking, centering the particles in
their selected boxes, 2D classification and determining their relative orientations
and/or 3D classification and 3D reconstruction. An example of image processing
and 3D reconstruction is shown in Figs. 3 and 4. The preprocessing step involves
CTF correction and image normalization [41]. As mentioned in the data collection
section, the data is collected at various defocus positions. As one gradually
increases the defocus (i.e., under focus), the contrast of the image proportionally
improves. Improvement in contrast comes at a cost, a loss in the higher spatial
frequencies (i.e., high-resolution information is lost) in the image, and in addition, it
introduces CTF modulation in the spatial frequencies of the image. Hence, the first
step in image processing is to calculate the lens defocus and astigmatism, which is
needed to correct the measured data for the CTF of the microscope [42, 43].
Software CTFFIND, ACE2, Gctf, or e2ctf.py [44–47] can be used to estimate the
CTF that is used for CTF corrections.
After CTF correction, the images are normalized to set the mean density of the
particles to zero and same standard deviation [41]. The particles are then manually
or auto-picked into boxes of 1.5–2.5x, the size of the largest axis of the particle
using suitable software. A guide for choosing the right box size is given at the
online documentation http://blake.bcm.edu/emanwiki/EMAN2/BoxSize. Number
384
R. Natesh