EMAN2 Tomography Workflow Tutorial

• This tutorial is best suited for EMAN2 built after 09/27/2018. Not everything described in the tutorial was functioning yet in the 2.22 release.

• This tutorial uses data from EMPIAR: EMPIAR 10064 (only the 4 mixed CTEM tilt series)

• Time estimates for each step are for a well-configured tomography workstation with a high-speed disk, 64+ GB of RAM and 16+ cores.
• The pixel size in the header of the files are incorrect as provided by EMPIAR. The correct Apix value (2.62) should be specified when importing the images.

Prepare input files (~2 minutes)

• Make a new empty folder for the project and 'cd' into that folder

• run e2projectmanager.py

• Make sure any EMAN2 commands you run are executed from within this folder (not any subfolder)
• You may use "Edit Project" from the Project menu to set default values for the project. While not required, it reduces later errors.
• Make sure the workflow mode is set to "TOMO" not "SPR"

• Import tilt series using Raw Data -> Import tilt series

  • Select the files, and make sure importation says copy

  • In this step you should enter the correct A/pix for your data in the apix box. For EMPIAR10064, this is 2.62. For your own data, you need to know this number. In later steps you should be able to use -1 (default) for apix.

  • If your tilt series isn't a single stack file, but is many individual images instead, you will need to use Generate tiltseries to build an image stack. This is not necessary for the tutorial data.

  • Once the options are set, press Launch

• It is critical that the filenames for your data not contain any spaces (replace with underscore) or periods (other than the final period used for the file extension). "" (double underscore) is also reserved for describing modified versions of the same file, and should not be used in your original files.

Tiltseries Alignment and Tomogram Reconstruction (20 min)

Alignment of the tilt-series is performed iteratively in conjunction with tomogram reconstruction. Tomograms are not normally reconstructed at full resolution, generally limited to 1k x 1k or 2k x 2k, but the tilt-series are aligned at full resolution. For high resolution subtomogram averaging, the raw tilt-series data is used, based on coordinates from particle picking in the downsampled tomograms. On a typical workstation reconstruction takes about 4-5 minutes per tomogram.

For the tutorial tilt-series:

• 3D Reconstruction -> Reconstruct Tomograms

• check alltiltseries

  • alternatively you can select one or more tilt series from the tiltseries folder

• check correctrot

• tltstep = 2

• clipz = 64

• If you wish to look at the intermediate aligned tilt-series and other files, uncheck notmp

  • This is not required for the remaining steps in the tutorial, but can be used to help understand how the tomogram alignment works. This requires significant additional disk space. You may consider doing this for only one tomogram.

  • In each tomorecon_XX folder

    • landmark_0X.txt has the location of the landmarks (which may be fiducials if present) in each iteration

    • samples_0X.hdf shows the top and side view of those landmarks

    • ptclali_0X.hdf has the trace of each landmark throughout the tilt series (they should stay at the center of image all the time if the alignment is good)

    • tomo_0X.hdf is the reconstruction after each iteration

• Launch

When working with your own data:

• Either specify the correct tltstep if the tilt series is in order from one extreme to the other, or specify the name of a rawtlt file (as produced by serialem/IMOD).

• While the program can automatically compute the orientation of the tilt axis, it is better to fill in the correct value in tltax since there is a handedness ambiguity in the tomogram if determined automatically.

• In most cases, the default npk should be fine. If fiducials are present, it is not necessary to adjust this number to match the number of fiducials. The program will use any high contrast areas it finds as potential landmarks.

• bytile should normally be selected, as it will normally produce better quality reconstructions at higher speed. If 2k or larger tomograms are created, memory consumption may be high, and you should check the program output for the anticipated RAM usage.

• The graphical interface only permits 1k or 2k reconstruction sizes. In our experience this is normally sufficient for segmentation or particle picking.

• When the sample is thin (purified protein, not cells), it is useful to check correctrot to automatically position tomograms flat in ice

• It can also be helpful with thin ice to specify a clipz value to generate thinner tomograms (perhaps 64 or 96 for a 1k tomogram).

CTF Estimation (10 min)

For the tutorial tilt-series:

• Subtomogram Averaging -> CTF Correction

• check alltiltseries

• Double check the voltage and cs

• Launch

When working with your own data:

• The first two options, dfrange and psrange indicate the defocus and phase shift range to search. They take the format of “start, end, step”, so “2, 5, .1” will search defocus from 2 to 5 um with a step size of 0.1. Units for phase shift is degrees.

• For images taken with volta phase plate, we usually have dfrange of “0.2,2,0.1” and psrange of “60,120,2”.

Note that this program is only estimating CTF parameters, taking tilt into account. It is not performing any phase-flipping corrections on whole tomograms. CTF correction is performed later as a per-particle process. This process requires metadata determined during tilt-series alignment, so it cannot be used with tomograms reconstructed using other software packages.

Tomogram annotation (optional)

• Since the tutorial data set is purified ribosomes, this step can be skipped for the tutorial data, and you can move on to template-based particle picking. For cells or other types of complex specimens, tomogram annotation can be used to produce locations of different types of objects.

This section is brief and is probably only useful for users already somewhat familiar with the tomogram annotation protocol. A more detailed tutorial of the subject can be found in TomoSeg. Note that some directory structure and user interfaces have changed in the latest version to match new tomogram workflow.

• Segmentation -> Preprocess tomograms

  • This step is not always necessary for tomograms reconstructed in EMAN2, but may slightly improve results.

• Segmentation -> Box Training References

  • This is a newer interface than previously used for this step. Select a few "Good" (regions containing the feature of interest) and "Bad" (regions not containing the feature of interest) boxes.
  • "~" and "1" on the keyboard can be used to move along the Z axis.
  • The new interface permits different types of features to be identified in a single session and in the same tomogram.

  • If the different features of interest have very different scale, it is always better to keep the box size at 64, and instead rescale the tomogram. As long as the rescaling is done using EMAN2 utilities, the program will correctly keep track of the geometry relative to the original tomogram & tilt series.

  • if you are doing this with the tutorial data, you would only have 2 classes of particles "ribo_good" and "ribo_bad".

  • When pressing Save all visible particles (box checked next to the class name) will be saved

• The rest of the annotation process remain unchanged from the original tutorial, except that now, all trained neural networks and training results are saved in the neuralnets folder, and all segmented maps are in the segmentations folder. You now only specify the label of the output file instead of the full file name.

• Segmentation -> Find particles from segmentation to turn segmented maps into particle coordinates.

  • Input both the tomogram and its corresponding segmentation, and the particles coordinates will be written into the metadata file.
  • Slightly tweaking the threshold parameters may yield better results.

  • featurename will become the label of particles generated. Those particles can be viewed in the particle picking step and processed in the following protocols.

Particle picking

Launch the boxer in Subtomogram averaging -> Manual boxing step. You can also launch it via the Tomogram evaluation step which is discussed later in this tutorial. Go through slices along z-axis using ‘~’ and ‘1’ on the keyboard and deleting boxes by holding Shift and click. The boxes are shown as circles, whose radii indicate the distance from the current slice to the center of the particles. You can rename the label of the boxes and create new types of boxes in the options window. Make sure the label is consistent for the same particle type in different tomograms, as it is used for particle extraction later. The box size can be set in the main window at the left bottom corner, in this case, 45 for the ribosomes (so the unbinned box size is 180). If you have got the particle coordinates from tomogram annotation, just take a look at the automatically generated particles here and remove some obvious bad ones. While you can save 3D particles from the GUI, there is no need to do so in this step. When you are satisfied with the result, simply close the window. You should have ~3000 particles from the 4 tomograms in the dataset.

If you skipped the tomogram annotation step, we will pick a few particles here to generate an initial model first, and use the initial model as a reference for template matching. Select 30-50 particles from a tomogram, then close the boxer window.

Particle extraction

In this step, the program will extract unbinned 2D particles from tilt series, perform per particle per tilt CTF correction, then reconstruct individual 3D particles. Select Extract particles from the left panel, check alltomograms, and specify the label of particle you want to extract. Make sure the label specified here corresponds to the label of particles from the particle boxer. If the box size is correct when you select particles from the GUI, you can leave boxsz_unbin as -1, so the program will keep that box size. You can adjust the value if you want to change the box size of the extracted particles. If your particles are deeply buried in other densities, using a bigger padtwod may help, but doing so may significantly increase the memory usage and slow down the process. With CTF information present, it generally does not hurt to check wiener, which filters the 2D particles by SSNR before reconstructing them in 3D. If you want to generate particles without CTF correction, check noctf. By default, the generated particles will have the same label as they are named in the boxer. If you want to have multiple types of particles, for example, with and without CTF correction, you can specify a different newlabel each time you launch the program. Specify a binning factor in shrink to produce downsampled particles if your memory/storage/CPU time is limited, but it may also limit the resolution you achieve at the end.

For the EMPIAR example, check alltomograms, and specify the label for the particles (either all ribosome particles or the ones for initial model); click Launch.

Then, go to Build set in the left panel, check allparticles, and click launch. This will generate particle sets, which are virtual particle stacks that consist of particles with the same label from different tomograms.

Initial model generation

To build an initial model from scratch, simply go to the Generate initial model step and input the particle list. If you wish the process to be faster, set shrink to 2-4. It is not necessary to change other options. The program is parallelized, but not in a standard EMAN2 way. To use more cores, you can enter a bigger number in batchsize. This will not make the program run faster but may make it converge to the correct answer faster. Also using more particles as input won’t make it run faster or slower either, so either input the full particle set if you have them, or the 30-50 particles you pick for the initial model. If the protein is known to be symmetrical, specify the correct symmetry. The program will not actually apply the symmetry (unless you check the applysym box, which is not recommended in general), but it will align the initial model to the symmetry axis so the following steps can work. For most situations, the default number of iterations (niter) of 5 is much more than needed.

In this ribosome dataset with shrink 3, the program should converge to a good initial model before the end of the first iteration, usually within 10 minutes. Output files are written in folders called sptsgd_XX. In the output folder, the file output.hdf is the current initial model, which is updated after each batch (so 10-20 times per iteration). So it is okay to stop the program early and use the file as an initial model once it looks good enough. While it would be good to have a better early stopping criterion, given the diversity of things in the cell, we have not come up with one yet.

Template matching

If you generated all particles with tomogram annotation already, skip this step. If not, go to Reference-based boxing, click Browse for tomograms to select all tomograms, and Browse the initial model generated in the previous step as reference. Specify the label of the output particles in label and set a maximum particle number per tomogram in nptcl (in the EMPIAR example, 800 should be fine), and click Launch.

After the program finishes, take a look at the particle coordinates from Manual boxing in the project manager or Tomogram evaluation, and manually remove the obvious bad boxes. This may perform worse than the tomogram annotation in excluding ice contamination and fiducial, but should still be fine for purified samples in this example. Once you are satisfied with the boxes, repeat the Particle extraction step using the label for the full particle set to generate all 3D particles.

Subtomogram refinement

Click 3D refinement from the left panel, and input both the particle set and the initial model generated from the last step as a reference. If there is a symmetry of the protein, make sure it is aligned to the symmetry axis before specifying the correct symmetry. If you are willing to split the even/odd set of particles and do a “gold-standard” refinement, specify a resolution number (usually 30-50) in goldstandard, so information beyond that resolution will be randomized independently in the reference for even and odd set. While it is good to have a reasonable mass for the molecular weight of protein (in kDa) and tarres for the target resolution, leaving them as default usually does not hurt. If you have a known structure factor in a .txt file, (you can compute it from a known structure via e2proc3d.py), specify it in setsf. localfilter will filter the averaged map by local resolution, which is especially useful when looking at things in cells where parts of proteins can be very flexible. This is almost always good to check when you want to push toward high resoluion. pkeep controls the fraction of particles that go into the final average. If you know there are many bad particles in the dataset, setting it to be a smaller number may help. Enter the number of threads you want to use in the thread option. Finally, click Launch and wait. For this dataset, it can take a few hours on a decent workstation. The results can be seen in the spt_XX folder. In the folder, threed_XX.hdf files are the main output map after each iteration, and fsc_masked/unmasked/masktight_XX.txt files are the FSC curves between even/odd half set under different masking. You should be able to get to 12-15Å resolution (cutoff 0.143) at this step using this dataset.

Subtilt refinement

Once the subtomogram refinement finishes, check the final map and FSC curves. In this dataset, you should be able to achieve a resolution of 13-15Å. Now we can refine the orientation of each individual subtilt, i.e. 2D particles from raw tilt series that are reconstructed into to the 3D particles, and push the resolution of the averaged map.

Click Sub-tilt refinement, choose the folder of the last subtomogram refinement and launch the program. You will need to specify the path to the spt_XX directory containing the last completed subtomogram refinement (typically just “spt_00” for example). Additionally, specify the iter you want to use as a starting point for sub-tilt refinement. If “-1” is specified, the program will attempt to locate the last complete iteration.

The default parameters should be generally fine for this dataset, though you may need to alter the parallel and threads options to use the number of CPU threads available on your computer. The niters value corresponds to the number of iterations of sub-tilt refinement you wish to perform. keep controls the fraction of particles that goes into the final map. If you are certain that tilt images beyond a certain angle (for example, 45 degrees) are radiation damaged, you can put 45 in maxalt, and specify a larger keep number. Otherwise, just use keep 0.5, so the program will judge the quality of subtilt images by their correlation to the averaged map and exclude worst 50% 2D particles.

Tomogram evaluation

This is a tool that helps you visualize your tomograms with their corresponding metadata, and launch other programs from it. It can be found via Analysis and visualization -> Evaluate tomograms. This can be used at any point of the workflow after tomogram reconstruction.

On the left is a list of tomograms in the project. Clicking the header of each column will sort the table by that attribute. #box is the number of boxes in the tomogram, loss is the average fiducial error in nm, and defocus is the average defocus of the tilt series. Do not be scared by large loss values here. Although the relative value of different tomograms (aligned with the same parameters) in the same project are correlated with tiltseries quality, the exact value here is not as meaningful. You can still get a subnanometer resolution subtomogram average from tilt series with a loss larger than 5 nm.

On the right, the image on the top shows the center slice of the tomogram. The Show2D button shows the selected tomogram in slices, ShowTilts shows the corresponding raw tilt series, and Boxer calls the 3D boxer. PlotLoss will plot the fiducial error per each tilt, and PlotCtf plot the defocus and phase shift at the center of each tilt image. Tiltparams is a bit more complicated. It plots a point list with 6 columns and a number of rows corresponding to the images in the selected tilt series. These are the alignment parameters for the tilt series. The columns represent tilt ID, translation along x and y-axis, tilt angle around y, x and z-axis correspondingly. You can adjust X Col and Y Col in the plot control panel (middle click the plot) to change the display. The first panel below the buttons are the types of particle and their numbers in the dataset. Check and uncheck the boxes will affect the number displayed in #box column on the left. The last box is reserved for comments for each tomogram. You can fill in any comments you have for the selected tomogram and it will be saved with other metadata of the tomogram for future references.

Refinement evaluation

This tool helps visualize and compare results from multiple subtomogram refinement runs. Launch it from Analysis and visualization -> Evaluate SPT refinement. In the GUI, you can look at all spt_XX and sptsgd_XX folders and compare their options and resulting maps. Switch between folder type using the menu at top right. Click the header of a column to sort the table by its content. Uncheck items in the list at bottom-right to hide corresponding columns. Clicking ShowBrowser will bring up the e2display.py browser in the folder of the selected row. PlotParams will plot the Euler angle distribution and other alignment parameters. The 8 columns in the plot are three Euler angles (az, alt, phi), translation in x,y,z, alignment score, and missing wedge coverage score. PlotFSCs will plot the FSC curve under tight mask from each iteration.