We carried out this work on a high-end electron microscope with a cryogenic stage. Relative to a microscope used for conventional SPA, several hardware and software modifications are required to collect low-noise spectral images. EELS spectra have to be collected pixel-bypixel in STEM mode, requiring the respective alignments and scanning functionality. We found an annular dark field detector useful for faster navigation and focusing. An energy spectrometer with an energy resolution of at least 1eV and stability over several days is desirable. The detector should fulfil several requirements: The readout noise should be low to allow accurate detection of single electron events. This is especially important for the very low-dose acquisitions used in this method, where there are only a few electron events in the core-loss region of a given spectrum. The dynamic range should be large so that the intense zero-loss peak can be recorded simultaneously. Additionally, high frame rates are required for reasonable acquisition speeds as a detector readout is required at every scanning position and, therefore, 16 million times per 4kx4k spectral image, making the acquisitions slow. All these requirements are met by modern hybrid pixel detectors. We used a Titan Krios G3 in STEM mode, equipped with conventional annular dark field detectors, a CEOS CEFID energy filter [17] and a post-filter Dectris ELA hybrid pixel detector [18] for spectral image acquisition. In an attempt to balance detector performance with aberration effects, we chose an accelertaion voltage of 200 kV for our experiments.

REEL-EM depends on large data sets to accumulate sufficient signal for elemental detetcion, which is substantially facilitated by automated acquisition of spectral images (ese Figure S1). For this, we use a combination of the microscope automation software SerialEM and the filter-control software Panta Rhei. SerialEM is used for setting up and navigating acquisiotin positions and for automatically focusing on a carbon area adjacent to the acquisition positoin. SerialEM then calls an external python script that triggers spectral image acquisition and saving in Panta Rhei via its RPC (remote procedure call) interface. After the acquisiiton, the stage is moved to the next position and autofocus is carried out by SerialEM while, in parallel, Panta Rhei is assembling and saving the previous spectral image. In this way, the acquisition can run for several days, producing one 4kx4k spectral image every 23 minutes. The speed of the data collection is limited by the pixel dwell time, which depends on the maximum frame rate of the detector, and by the assembly and saving time for a spectral image in Panta Rhei. We acquired the spectral images with a dwell time of 30 µs (~33 kHz), which is beyond the specification for the Dectris ELA, but we did not observe data loss in this mode. We found that the addition of gold fiducials to the carbon film aids the autofocus routine, which is, in our experience, much less robust in STEM than in TEM.

To calculate particle poses, a reference image is required, which could be derived in a number of ways. A conventional STEM detector records amplitude contrast signals due to differences in scattering angle, with elastic scattering angles being typically higher than inelastic scattering angles. The signal of elastically scattered electrons can be collected either by recording their presence at higher scattering angle, forming an annular dark field (ADF) image, or their absence at lower angles, forming a bright-field image. In our experiments, the camera length was adjusted so that the full bright-field disk entered the entrance aperture of the energy filter, excluding (mostly elastically-scattered) electrons scattered at higher angles (convergence angle { = 6 mrad; collection angle ³ = 8 mrad). Summing the counts in the zero-loss portion of the spectral image therefore gives an elastic bright field (EBF) image. It is possible to collect an ADF image in parallel to spectral image acquisition. For a small data set of 3,086 particles of worm haemoglobin (erythrocruorin) fromLumbricus terrestris, we compared the quality of reconstructions, aligned with a Class3D job, from simultaneously collected dark-field and bright-field images. The collection angle range for the dark field detector was 15 3 150 mrad. We also compared a reconstruction from images for which the ADF images were subtracted from the EBF images (Figure 2). The bright-field images performed best, resolving most features in the reconstruction. The dark-field and mixed reconstructions, in comparison, appear more noise-dominated. This is most likely a consequence of larger amounts of detector noise in the ADF than in the EBF images. For the larger data sets, we therefore did not acquire simultaneous dark-field images and used only the EBF images as reference.

From a data set of 1,142 spectral images of rabbit Ryanodine Receptor 1 (RyR1), we extracted EBF images from an energy region of ±3.6 eV. We picked 460,651 particles with a manually trained model in crYOLO [19], which we extracted and subjected to 2D classification for cleaning, 3D classification for pre-alignment and refinement in REIOLN [20], using an initial model generated from the data, low-pass filtered to 60 Å, as reference. No contrast transfer function (CTF) correction was applied during processing. The refinement yielded a reconstruction at 24 Å resolution after post-processing (Figure 3). The same procedure was performed on a smaller data set of worm haemoglobin (WH). For this sample, 16,648 EBF particle images yielded a resolution of 27 Å after post-processing (Figure S2). These refinements provided particle positions and poses to allow reconstruction of energyloss data.

A schematic of the reconstruction procedure is shown in Figure S3. Star files from the reference refinements were manipulated to point to energy-loss images for each 0.77 eV energy bin of the spectral images. For each energy bin, particles were extracted and reconstructed to a 3D volume, using the command relion_reconstruct. To maintain interpretable EELS spectra after reconstruction, the volumes should only undergo linear transforms so that an external intensity scale is maintained. These terms are not metby relion_reconstruct, as this function sets the Fourier origin voxel to zero, thereby normalising the intensity of the reconstruction. We compiled a version of RELION without this normalisation and used this version for reconstruction. This modification comes at a cost of artefactual density, which is strongest at the corners of the reconstructed box (Figure S4). This effect can be minimised by oversampling the Fourier volume using the pad option. We found that a padding factor of 8 renders the intensity of the artefact sufficiently low for it not to be visible at threshold levels used for inspecting the reconstructions, although this resulted in noisier spectra. For this reason, all spectra displayed in this work are from reconstructions performed with 2-fold zero-padding and all displayed volumes from reconstructions performed with 8-fold zero-padding for better visualisation. The modified reconstruction procedure maintains the intensity profile of the EELS spectrum of the constituent particle images (Figure 4).

Analysing the spectrum summed across all voxels of the RyR1 reconstructions, ionisation edges for the most abundant elements are clearly visible: The K-edges of carbon at 284 eV, oxygen at 532 eV (with a small pre-peak indicating the expected presence of gaseous oxygen due to radiation damage [21]), and nitrogen at 401 eV, as well as the L2,3-edges of the buffer components chlorine at 200 eV, potassium at 294 eV, and phosphorus at 132 eV (Figure 5). Movie 1 shows a sequence of all energy-loss reconstructions. There are evident changes in the 3D localisation of the signal with changing energy loss. These changes coincide with the major features in the spectrum: In the core-loss region prior to the carbon edge (237.5 3 242.1 eV), the intensity shows no apparent correlation with the complex. In contrast, at the carbon edge (283.2 3 287.8 eV), the density colocalises with the protein and the micelle. At the oxygen edge (540.4 3 545.0 eV), the density shows an inverse correlation with the complex, in line with the higher oxygen concentration in the solvent (Figure 5). These differences reflect the known elemental distributions of the complex. In addition to elemental features, the low-loss region (&E f 50 eV) of the spectrum contains specific plasmon absorbances that are spatially resolved in the reconstructions (Figure S5).

In principle, EELS analysis can detect all elements other than hydrogen (except as H2). Of the known elements in our sample we detect all except hydrogen, zinc, sulphur, and calcium. As the energy range and spectrum positioning on the detector were optimised for other edges in this data set, we do not expect to detect zinc because its major absorption edge (1020 eV, 1043 eV) lies beyond our chosen energy range, or sulphur (165 eV) because the associated energy loss coincides with an artefact at the detector tile edge (see for example Figure 5). The spectrum of a 4x4x4-voxel region near the known calcium-binding site formed by residues including E3893, E3967, and T5001 [22] does not show a calcium L 2,3-edge signal discernible above noise, indicating that we have not reached single-atom sensitivity with this data set (Figure 5B). With 460,651 4-fold symmetric particles, collected at a dose of 92 e-/Å2, the effective accumulated dose for the reconstruction is around 1.7*180e-/Å2 which exceeds an estimated minimum dose of 107 e-/Å2 [3] by more than an order of magnitude. The spectrum of a single voxel in the protein, which contains on average 1.7 atoms of carbon, clearly shows a carbon edge (Figure 5C), giving an indication that we are approaching the range of single-atom sensitivity. An important difference to the detection of calcium aits binding site is that the carbon signal is less affected by inaccuracies in particel poses, as surrounding voxels also contain carbon.

Spectral reconstructions of worm haemoglobin were produced in the same way as for RyR1. The spectrum of the sum of all voxel values of the reconstructions similarly shows carbon, oxygen, and nitrogen K-edges, as well as L2,3-edges for chlorine, potassium, and phosphorus (Figure S6). Movie 2 shows the full series of energy-loss reconstructions.

The resolution of reconstructed energy-loss volumes can be estimated by halfmap Fourier shell correlation (FSC) analysis. For the sum of spectral reconstructions at the carbon edge (283.2 3 287.8 eV), the FSC between the sums of reconstructed halfmaps was calculated (Figure 6A). As the halves were separated according to the gold standard during refinement, we used a correlation cut-off of 0.143, yielding a resolution of 39.8 Å.

To obtain an element-specific signal by EELS analysis, it is necessary to account for the background signal. In the case of carbon, the localisation of the signal with the protein in the spectral reconstructions has a clear onset at the carbon edge, showing that the elemental signal is strong enough to dominate above the background at this edge (Figure 5 C,D and Movies 1 and 2). For smaller edges, an element-specific signal can only be visualised after accounting for the signal of the decaying background in the EELS spectra. For our 4D spectral volumes, this requires per-voxel background subtraction at the relevant edge(s). In order to assess whether such a background subtraction is feasible with our data, we performed per-voxel background subtraction at the carbon and nitrogen K-edges. Individual voxel spectra show a relatively high level of noise, especially at larger energy losses (Figure 6B). To ensure sufficient signal for accurate background modelling, we applied Gaussian filtering with a standard deviation of 2 voxels to the reconstructed volumes (Figure 6B). For each voxel, we then fitted the region between 160 eV and 250 eV for carbon and between 325 eV and 375 eV for nitrogen with a power law function (Figure 6B), and subtracted the extrapolated fit from the subsequent energy bins. True elemental volumes were then generated by summing the background-subtracted values in the edge regions of carbon (283.2 3 287.8 eV) and nitrogen (403.3 3 407.9 eV). For the carbon edge, the background-subtracted volume appears very similar to the non-subtracted summed volume (Figure 6C). A larger difference is apparent for the maps at the nitrogen edge (Figure 6D). The nitrogen K-edge lies on the long tail of the carbon K-edge, so that the reconstructed volumes at the nitrogen edge also contain scattering contributions from carbon atoms. Comparing the unsubtracted and background-subtracted reconstructions, the reconstruction without background subtraction shows stronger density in the region of the micelle than the map with background subtraction, when adjusting to similar thresholds for the protein (Figure 6E). This reflects the higher carbon:nitrogen ratio of the micelle in comparison to the protein. The micelle is composed mainly of CHAPS for which the ratio is 16:1, while it is 3.6:1 for RyR1.

This result demonstrates the importance and feasibility of performing background subtraction on REEL-EM volumes, in order to derive specific elemental distributions from reconstructed energy-loss volumes.

Rabbit RyR1 was purified as described previously [31] with modifications. All steps were performed at 4 °C. In brief, 200 g of frozen rabbit skeletal muscle tissues (Pel-Freez Biologicals) were blended for 120 s in 2 x 400 mL of buffer A (20 mM Tris-maleate pH 6.8, 10% sucrose, 1 mM DTT, 1 mM EDTA, 0.2 mM PMSF, 1 mM benzamidine) and centrifuged at 3,000 g for 10 min. The supernatant was filtered through a cheesecloth and ultracentrifuged at 200,000 g for 45 min. The membrane pellet was collected, flash-frozen in liquid nitrogen, and stored at -70 °C for later use. The membrane fraction after thawing was solubilized in buffer B (50 mM HEPES pH 7.5, 1 M NaCl, 1 mM EGTA, 2 mM TCEP, 0.1 mM PMSF, 1% CHAPS, 0.2% soybean L-³-phosphatidylcholine (PC), 1:1000 of protease inhibitor cocktail (PIC; Millipore Sigma 539134)) for 1 hr. Then, an equal volume of buffer C (buffer B lacking 1 M NaCl) and His-GST-FKBP12.6 (10 mg) were added and this was stirred for another hour. The solubilized mixture was ultracentrifuged at 200,000 g for 45 min. The supernatant was mixed with 3 mL of Glutathione Sepharose 4B resin (Cytiva) and stirred for 1 hour. The resin was poured into a gravity column, washed with buffer C (50 mM HEPES pH 7.5, 0.5 M NaCl, 2 mM EGTA, 2 mM TCEP, 0.1 mM PMSF, 0.5% CHAPS, 0.1% soybean PC, 1:1000 of PIC) and eluted with buffer C plus 20 mM glutathione. The His-GST tag was removed by overnight incubation with Tobacco Etch Virus (TEV) protease (1 mg). The cleavage product was concentrated and applied to a Superose 6 Increase 10/300 GL column (Cytiva) in buffer D (20 mM HEPES-KOH pH 7.5, 250 mM KCl, 40 µM free Ca2+ buffered with 2 mM EGTA, 0.375% CHAPS, 0.001% DOPC, 2 mM TCEP, 0.1 mM PMSF, 1:1000 PIC). Peak fractions containing RyR1 were combined and concentrated to ~15 mg/mL (measured by NanoDrop). RyR1-FKBP12.6 sample was mixed with calmodulin (CaM) (equilibrated in Buffer D) to a final concentration of 12 mg/mL and 100 µM, respectively. Holey carbon grids (Quantifoil Cu 300 mesh, R2/2) were prepared with fiducials by glow-discharging, adding 3 µL of 10 nm Protein A Immunogold solution and blotting from the back. The grids were left to dry overnight in a desiccator. 2.5 µL of RyR1-FKBP12.6-CaM sample were applied and blotted for 3 s (blot force of 6 to 9) using ashless blotting paper and the grids were subsequently plunge-frozen in liquid ethane using a Vitrobot Mark IV (Thermo Fisher Scientific) at 4 °C and 100% humidity.

For the worm haemoglobin sample, earth worms (Canadian Night Crawler) were purchased from a fishing supplier. Blood was extracted from the seventh segment and diluted with saline solution of 50 mM NaCl and 10 mM MgCl2. C-flat grids with a hole size of 1.2 µm and a hole spacing of 1.3 µm were used for this sample. The grids were prepared with fiducials by glow-discharging, adding 3µL of 10 nm Protein A Immunogold solution to the carbon and blotting from the back. The grids were left to dry over a minimum of one night before they were used for sample preparation. They were then again glow-discharged and 3 µL of sample were applied, before blotting and plunge-freezing in liquid ethane in a Vitrobot Mark IV, using blot force 0 and 8s blot time at 4 °C and 100% humidity.

Data were recorded on a Titan Krios G3 with STEM capability, equipped with HAADF (Fishione) and ADF (Thermo Fisher Scientific) detectors, a CEFID energy filter [17] and a post-filter Dectris ELA hybrid pixel detector [18]. For the acquisition of spectral images, csan generation, beam-blanking and data compression were performed by the CEOS software Panta Rhei, with scanning controlled through an external scan generator (TVIPS). For acquisition of overview, alignment, and focus images, the Thermo Fisher Scientific scan generation and beam blanking were used. The experiments were performed at an acceleration voltage of 200 kV, convergence angle of 6 mrad, spot size 10, gun lens 3, 50 µm C2 aperture, and a magnification of 80 kx, corresponding to a pixel size of 3.65 Å. The collection angle for electrons entering the energy filter corresponded to 8 mrad. This was limited by the hole in the dark field detector which was inserted during acquisition, as this was smaller than the entrance aperture of the filter. To determine the dose rate, we first calibrated the avreage number of pixels activated per electron hit by looking at a sparse region of a spectrum, where single electron hits were clearly discernible as clusters of activated pixels. This is enabled by the linear count response of the detector to electrons up to 5x106 counts/pixel/s which was not exceeded in the experiment. We then divided the total electron counts per time in an image of the beam over vacuum by this factor and the pixel area. The pixel size was calibrated by maximising the 0.5 Fourier shell correlation between an extracted bright-field reconstruction and a TEM reconstruction with a carefully calibrated voxel size of the same sample. For maximum frame rates of the detector of up to 33333 Hz, only a 66x1024 pixel area region of interest (ROI) of the detector was read out. The recorded spectra were then summed in the non-dispersive direction by Panta Rhei before saving of spectral images in hyperspy format with lossless compression. The energy filter was set to disperse 770 eV (from -25.4 eV to 745.6 eV) over the longer dimension of the detector, yielding energy bins of 0.75 eV. The bin size was later adjusted by a factor of 532/515 on the basis of the position of the oxygen edge relative to the zero-loss peak. STEM direct alignments were performed manually on an area of carbon film adjacent to the acquisition area, immediately before beginning data acquisition. Filter alignments were performed over vacuum using the automated tuning routine provided in Panta Rhei.

The acquisition was automated using SerialEM [32] for navigation and autofocus and using Panta Rhei for spectral image acquisition and saving. In SerialEM, overview images of the entire grids were acquired in TEM mode as stitched maps at low magnification. Suitable squares were then selected and recorded at an intermediate magnification in STEM mode, on the DF2 darkfield detector. Intact holes were selected as acquisition positions on the grid square maps. An automated acquisition routine was then triggered, which functions as follows: At each acquisition position, a SerialEM script moves the stage to the coordinates and the hole is further aligned on the basis of a low-dose image from the DF2 detector. The STEM autofocus routine is then run on an adjacent area of carbon and an external RPC script is triggered. SerialEM relies on the internal scan generator of the microscope (Thermo Fisher). The RPC script calls functions for spectral image acquisition and saving in Panta Rhei. This requires a switch to the external (TVIPS) scan generator which is automatically performed by Panta Rhei. Files are saved in hyperspy format. After a wait time of 12 minutes, when the current spectral image acquisition has been completed but data are still being processed and saved by Panta Rhei, the SerialEM script resumes and moves the stage to the next imaging position where the autofocus routine is again started. A wait time is set to ensure a minimum of 23 minutes between one call of the RPC script and the next to ensure that the previous file is saved before starting a new acquisition. 1142 4k x 4k x 1024 spectral images of RyR1 and 61 spectral images of WH, of which 7 included a simultaneous dark-field image, were collected with a dwell time of 30 µs per pxiel at a calibrated total dose of 92 e-/Å2. The collection angle for the dark field image was 15 3 150 mrad.

Using the Hyperspy framework [33] (version 1.6.4), the mean zero-loss peak (ZLP) position was calculated for every pixel in each spectral image by determining the location of maximum intensity. We found that the position was stable within the instrumental limits on energy resolution throughout each spectral image, so the mean position for each spectral image was used for the correction of the ZLP position. Elastic bright-field images were extracted as tiff files from an energy range of +- 3.5 eV around the zero-loss peak centre. This encompasses 99 % of the peak. Within these micrographs, particle positions were determi ned using crYOLO [19] with a manually trained model. Particles were extracted and further processed in RELION-4 [20]. No motion correction, dose weighting, or CTF correction was applied. 2D classification was applied for additional data set cleaning. 460,651 particleswere selected for the RyR1 data set and 16,648 particles for the WH data set. The selected partciles were pre-aligned using a Class3D job with a T-value of 1 and 40 iterations, using a 60 Å 3 lowpass filtered initial model, generated from the data for each data set, as reference, and then subjected to 3D refinement, using a soft solvent mask. C4 symmetry was applied for RyR1 and D6 symmetry for WH. The refinement resolutions were determined by goldstandard FSC calculation in a PostProcess job.

For the spectral images for which a simultaneous dark-field image was collected, 3086 particles were picked on EBF images, generated as above. An additional set of images was produced by subtracting the dark-field images from the corresponding ADF images. The particles were extracted from all three sets of images and independently subjected to alignment by 3D classification with 40 iterations. For each map, an FSC to a map on the basis of PDB-5M3L [34, 35], generated by ChimeraX9s [36] molmap command, was calculated and resolutions were estimated from a 0.5 cutoff.

In a second Hyperspy script, energy-loss micrographs were extracted as tiff files from the spectral images for both data sets. For each energy bin, all particles were reextracted with the pose information from the Refine3D star file. No normalisation was applied to the particle images. For reconstruction, RELION was recompiled without line 714 in src/reconstructor.cpp, which otherwise sets the Fourier origin voxel of the reconstruction box to zero. A volume was then reconstructed for each energy, using the command relion_reconstruct with two-fold or eight-fold padding and full gridding. A single reconstruction (at 284 eV) was also made without any padding for comparison. For the maps of the pre-edge region, the carbon edge, and the oxygen edge, the volumes for 237.5 3 242.1 eV, 283.2 3 287.8 eV, and 540.4 3 545.0 eV, respectively, were summed. For display, a Gaussian filter with a standard deviation of 2 voxels was applied. For the RyR1 data set, halfmaps were reconstructed for the volumes between 283.2 and 287.8 eV by applying the halfmap option in relion_reconstruct, such that the halves were separated in the same way as for the EBF reference reconstructions. The maps were then summed for all of these volumes for each half data set and an FSC curve was calculated between the sums of the halfmaps, using relion_postprocess and a suitable mask. The mean intensities of the particles at each energy were determined from all pixels in all particle images that contributed to each reconstruction of the RyR1 data set. In Figure 4 they are compared to the mean voxel values of the resulting reconstructions.

Volumes and spectra were analysed using ChimeraX [36] and the Hyperspy library, respectively. Plots were created in python, using the matplotlib library [37]. The position of the expected calcium-binding site for RyR1 in relation to the reconstructed volumes was determined on the basis of PDB-5TAQ [22, 38], which was rigid-body-fitted into the EBF reference reconstruction. The average number of carbon atoms in a single voxel of RyR1 was determined by counting all atoms in a sphere located in a protein-dense portion of the RyR1 atomic model (PDB-5TAQ [22, 38]), dividing by the volume of the sphere, and multiplying by the volume of a voxel in the reconstruction.

Movies were produced in ChimeraX by morphing between the series of reconstructions, gaussian-filtered with a standard deviation of 7.3 Å, using one frame per reconstruction. The option <constant volume= was set to <true=. Spectrum and slider were added in the programme Adobe Premier Pro.

For background subtraction, the spectral volumes were processed with Hyperspy. Volumes were first individually Gaussian filtered with a standard deviation of 2 voxels and then combined into a 4-dimensional EELS Object. Background fitting was performed with a power law function and the fast-fitting option disabled in the region of 186 3 273 eV for carbon and 335 3 387 eV for nitrogen. The background-subtracted volumes at each energy were then individually saved as mrc files. For the carbon map, the subtracted volumes were summed from 283.2 3 287.8 eV and for nitrogen from 403.3 3 407.9 eV. For better visualisation, Gaussian filtering was again applied to the background-subtracted nitrogen map.

Preparation " " "

Align microscope and filter in all required settings Record low magnification map of full grid (in STEM or TEM) in SerialEM Set up acquisition positions and record modes in SerialEM SerialEM

RPC script

PantaRhei

Go to next acquisition position Center on hole Autofocus next to hole Set magnification Call script

Set incremental file name Call spectral image acquisition Call save " "

Acquire spectral image (hybrid pixel detector) Save spectral image Figure S1 Schematic of the procedure for automated collection of spectral images using the microscope control software SerialEM in communication with the filter software Panta Rhei viaits RPC interface. After preparation steps, as indicated, automated acquisition is started.For every acquisition position, SerialEM prepares for the acquisition, then calls an RPC script which triggers Panta Rhei to acquire and save spectral images. Reference reconstruction of WH.

Refined reconstruction of WH from a data set of 16,648 particle images which were extracted from the zero-loss region of spectral images. The map shows a good fit 3 albeit at low resolution 3 to the published model of earthworm haemoglobin (PDB-5M3L [34, 35]) displayed as cartoon model. (B) An exemplary extracted EBF micrograph. (C) The gold standard Fourier shell correlation for this reconstruction has a 0.143 cut-off at 27 Å. Reconstructions from the plasmon region of the energy-loss spectrum show colocalization with solvent, detergent micelle or protein locations at different energy losses. (A) Spectrum of the summed voxels of reconstructions in the plasmon region. (B-D) Summed reconstruction for the energy windows (B) 8.1 3 11.2 eV, (C) 15.1 3 18.2 eV and (D) 19.0 3 22.1, overlaid on PDB-5TAQ [22, 38], showing different localisations across the volume.

Movie 1. A morph through the full spectrum of energy loss reconstructions for RyR1. Volumes are displayed with Gaussian filtering (standard deviation 7.3 Å) for better visualisation. The thresholds for the maps were automatically adjusted during morph creation to keep the displayed volume constant.

Movie 2. A morph through the full spectrum of energy loss reconstructions for WH. Volumes are displayed with Gaussian filtering (standard deviation 7.3 Å) for better visualisation. The thresholds for the maps were automatically adjusted during morph creation to keep the displayed volume constant. 33. 34. 37. 38.