Processing a High-Resolution Cryo-Tilt Series with Fiducials

(IMOD 5.1)

University of Colorado, Boulder

 

This is a guided introduction to generating a tomogram from a cryo-tilt series to be used for high-resolution subvolume averaging, with gold beads as fiducial markers for alignment.  It presents the most important concepts and details and provides brief explanations of some points.  For more details, consult the Tomography Guide, which you can open from the Help menu in Etomo.  It is also advisable to read through relevant sections of the Tomography Guide before trying to process your own tilt series.  If you have never processed a tilt series in Etomo before, you may want to do the dual-axis tutorial before this example, especially if you have difficulty following the steps below without shots of how the screen should appear.

The example data set is from the study of HIV virus-like particles in Schur et al., 2016, Science 353:506-598, based on the data set TS_43 from the EMPIAR-10164 deposition.  The tilt series was produced by aligning the super-resolution K2 frames with Alignframes, using the command
     alignframes -pair 8 -bin -1,2 -mdoc TS_43.mrc.mdoc TS43.mrc -adj -dty 4 -norm -ref 5 -gpu 0.
This data set is set up to be used as an example for measuring CTF, and the reconstruction will be done in a subdirectory.

First, a few points on conventions: labels in the Etomo or 3dmod interface are shown in Bold, and entries in fields are shown in italics.  For mouse operations in the Zap window in 3dmod, the buttons are referred to as "first", "second", and "third" because the buttons can be remapped in 3dmod.  If you have not changed the mapping, this corresponds to left, middle, and right; otherwise, it refers to whatever you have chosen to be the first, second, and third buttons.   

Getting started:

 

Tomogram setup:

In this initial step, we define some features of the data set and create the files needed for processing.

Pre-processing:

This step is often needed to remove artifacts in the images, generally due to "hot pixels" in a direct detector camera.  These artifacts will produce streaks in a reconstruction and can also make it harder to see the image features, which have a much smaller dynamic range than the artifacts.

Coarse Alignment

In this step, we use image cross-correlation to align successive images, which makes it easier to track fiducial markers.

Fiducial Model Generation:

In this step, the positions of selected gold markers are found on each image, or as many of them as possible, which allows a more accurate alignment to be obtained.

Fine Alignment:

Next, the bead positions are fit to a mathematical model of specimen movements.  The model predicts a position for each bead on each view, and the mean distance between the predicted and actual positions is referred to as the "mean residual error".  By obtaining a solution with some points left out, a different error can be computed between the predicted and actual positions of points not included in the fit, the "leave-out" error.  It requires many alignment runs with different points left out to obtain a reliable estimate of the leave-out error. Since this error is an estimate of how well the solution generally predicts the positions of structures other than the beads that happen to be included in the fit, it is a better measure of the quality of a solution than the mean residual.  The main problem with the mean residual is that solving for more variables will always give a better fit to the points, but using too many variables will fit excessively to the random errors in the positions and do a worse job of predicting the positions of other points in the specimen. You should always use the leave-out error to assess the value of any change in the alignment parameters. However, either of these errors will let you find and correct badly modeled points.  We will go through a preliminary adjustment of parameters to improve the fit, then fix some of the points with the largest errors, then do a final optimization of the parameters. 

 

Initial parameter adjustment

The reason to do at least some adjustment of parameters at the start is to minimize the number of points that have large residuals because the model is not complex enough to fit them, so that we can focus on finding points that are not well-centered.

Correcting model points

In some situations, the checking of model points can be skipped by using a method called "robust fitting", which automatically gives less weight to or even eliminates the points most likely to be at incorrect positions.  However, this method often does not help much (as we will see), and some manual checking of positions is appropriate when doing high-resolution work.

Optimizing parameter settings

The goal of this step is to avoid overfitting to the random position errors by reducing the number of variables being solved for.  It is important but simple to do.

Tomogram Positioning

The goal of this step is to set angles and an offset in Z so that the specimen is level in the X-Z plane and centered in Z in the computed volume, thus minimizing the computational effort.

 

Final Aligned Stack Creation and CTF Correction:

Gold Erasing:

Gold beads are by far the densest items in cryo-reconstructions and they cast artifactual rays that are about as dense as the biological features.  To minimize this effect, it is often desirable to remove the beads from the projection images before reconstruction.

2D Filtering for Dose Weighting:

Useful high-frequency information is progressively lost due to beam damage over the course of tilt series acquisition. Dose-dependent attenuation of these frequencies to reduce the residual noise is known as dose weighting.

Tomogram generation:

At last, you can compute the tomogram.  Notice that the "cryoAccurate.adoc" template has selected Super-sample by 2, which will eliminate most of the artifactual rays that would appear in an FFT of an XZ tomogram slice; and it has set the Standard Gaussian cutoff to 0.425/pixel, higher than the usual 0.35, to preserve more of the high-frequency information.

Post-processing:

In this step, you can trim away unneeded regions, convert the tomogram to bytes to save time and space, and reorient the tomogram so that the slices stored in the file are in X/Y planes instead of X/Z planes.  Even if you do not want to trim or convert to bytes, you should always go through this step to get a reoriented tomogram, which will work better with other programs.  Unfortunately, it is difficult see the VLPs well enough to set reliable trimming limits, even if the tomogram is loaded with typical amounts of binning.  Here is a procedure that will work:

Making a Reduced, Filtered Volume

For particle selection, a much less noisy volume will be needed.  A good starting point is a volume reduced by 4, and filtered with the deconvolution filter.

Clean Up:

In most cases, there is no need for the intermediate files from processing.  This step allows you to remove these files and leave all of the information from which they could easily be recreated if necessary.  The original raw tilt series stack can also "archived" by compressing its difference from the current stack; this operation is reversible.