User-friendly electron microscopy protocols for the visualization of biological macromolecular complexes in three dimensions: Visualization of planta clathrin-coated vesicles at ultrastructural resolution

Biological systems are the sum of their dynamic 3-dimensional (3D) parts. Therefore, it is critical to study biological structures in 3D and at high resolutions to gain insights into their physiological functions. Electron microscopy of metal replicas of unroofed cells and isolated organelles has been a key technique to visualize intracellular structures at nanometer resolution. However, many of these protocols require specialized equipment and personnel to complete them. Here we present novel accessible protocols to analyze biological structures in unroofed cells and biochemically isolated organelles in 3D and at nanometer resolutions, focusing on Arabidopsis clathrin-coated vesicles (CCVs) - an essential trafficking organelle lacking detailed structural characterization due to their low preservation in classical electron microscopy techniques. First, we establish a protocol to visualize CCVs in unroofed cells using scanning-transmission electron microscopy (STEM) tomography, providing sufficient resolution to define the clathrin coat arrangements. Critically, the samples are prepared directly on electron microscopy grids, removing the requirement to use extremely corrosive acids, thereby enabling the use of this protocol in any electron microscopy lab. Secondly, we demonstrate this standardized sample preparation allows the direct comparison of isolated CCV samples with those visualized in cells. Finally, to facilitate the high-throughput and robust screening of metal replicated samples, we provide a deep learning analysis workflow to screen the ‘pseudo 3D’ morphology of CCVs imaged with 2D modalities. Overall, we present accessible ways to examine the 3D structure of biological samples and provide novel insights into the structure of plant CCVs.

honeycomb pattern) to create a 3D spherical coat/cage covering the vesicle (11-13). These CCVs are 48 essential trafficking organelles formed during clathrin-mediated endocytosis, where they mediate 49 cargo entry into the cell, and post Golgi-trafficking, to regulate protein sorting (8,(14)(15)(16). In stark 50 contrast, while CCVs are also essential in plants (16,17), we know very little about their structural and 51 functional details. A major reason for this is because many of the standard EM sample preparation 52 methods established in other model systems fail to reliably preserve planta CCVs. Furthermore, the 53 TEM images of plant CCVs have been limited to planar two-dimensional (2D) views (17)(18)(19). Therefore, 54 due to a lack of suitable protocols, a robust examination of the 3D structures of plant CCVs has been 55 lacking. 56 To resolve the reliance on protocols which require specialist handling of acids, and facilitate 57 the 3D analysis of plant CCVs at high resolution, we set out to establish protocols accessible to any 58 routine EM lab. Using Arabidopsis CCVs as a model organelle, we describe an accessible novel metal 59 replication protocol which can be performed directly on an EM grid; thereby removing the 60 for preparing unroofed cells to prepare and visualize purified CCV preparations, allowing the direct 126 comparison of isolated and in vivo organelles, such as purified CCVs with bona fide endocytic CCVs. 127 CCVs were isolated from undifferentiated Arabidopsis suspension cell cultures (22), and 128 attached directly to glow discharged and carbon coated EM mesh-grids. The samples were then 129 processed with the same protocol as unroofed cells. Imaging the metal replicas of purified CCVs with 130 STEM tomography allowed the production of 3D reconstructions of the isolated CCVs ( Fig. 2A, B and  131 Movie S2), enabling their quantitative structural analysis (Fig. 2C). We found that the average isolated 132 CCV was 71.06 ± 1.65 nm in diameter, 1547.45 ± 110.64 µm 3 in volume and comprised of 17 clathrin 133 panels (6 pentagons, 4 hexagons, and 7 not fully visibly closed at the base of the CCV) with a branching 134 arm length of 18.22 ± 0.14 nm (Table S2). 135 This demonstrates the versatility of the on-grid metal replication protocol and provides a 136 standardized protocol to structurally characterize CCVs from different biological sources using STEM 137 tomography. 138

High throughput pseudo 3D morphology screening of CCVs (Protocol 3) 139
While tomographic reconstructions of single CCVs provides the direct visualization of their 3D 140 structure and morphology at high resolution, it is a low throughput approach. Thus, to increase our 141 ability to examine large numbers of CCV structures, we developed a deep learning-based workflow to 142 analyze the morphology of CCVs from images acquired at lower resolutions and in a single plane. This 143 morphologic analysis provides a rapid and robust 'pseudo 3D' analyses of CCVs and their formation in 144 a high throughput and unbiased manner. 145 To develop this method, we used images of metal replicas of unroofed protoplasts derived 146 from Arabidopsis root cells attached to glass coverslips (6). Here, as the metal replicas were imaged 147 in 2D with a large field of view using scanning electron microscopy (SEM), each image contained many 148 visible CCVs enabling the high throughput visualization of CCVs. To characterize the morphology of 149 the CCVs, we quantify various object features: such as area, maximum and minimum diameters, and 150 average grey value. To date, such analysis has relied upon manual segmentation of the CCVs by the 151 experimenter. To increase the throughput and reproducibility of the morphology quantification of 152 CCVs, we used deep learning to create an accurate CCV segmentation model. To do this, we created a 153 comprehensive training set of image pairs; consisting of raw images together with these same images 154 where the CCVs are manually annotated (Fig. 3A). This training dataset was then used to train a state-155 of-the-art neural network model called Cellpose to generate an accurate CCV prediction model (23). 156 To increase the robustness and the ability of the model to predict all possible CCV structures, the 157 training set included images of metal replicas of unroofed cells derived from wild-type plants and from 158 a mutant that produces clathrin plaques (which are absent from wild type cells (6,20,24)). The trained 159 model was then used to automatically detect CCVs in additional images which had not previously been 160 seen by the model ('unseen images') ( Fig. 3B). To validate the model predictions, we compared them 161 to manual segmentations on a dataset of CCVs from wild-type cells. From 17 SEM images of metal 162 replicas of unroofed cells, manual segmentation identified 234 CCVs, whereas the automated method 163 successfully predicted 232 (Table S3). To determine if the CCV segmentations from our model matched 164 those made manually, we calculated an average pixel overlap of 83% between the manual and 165 automated CCV segmentations using an intersection over union (IoU) calculation (Fig. S2), indicating 166 the Cellpose model predicted the same CCV structures and with high degree of accuracy. The 167 predicted CCV segmentations were then exported as regions of interest (ROIs) to FIJI and quantified. 168 This 'FIJI analysis' step was not automated to provide a checkpoint for users to visualize and confirm 169 the automated CCV segmentations. We found that the average areas reported by manual and 170 automated analyses were not significantly different (t-test, p = 0.35) (4.93 ± 0.21 µm 2 (dimeter of 171 79.24 nm) for manual and 5.24 ± 0.25 µm 2 (dimeter of 81.71 nm) for the automated segmentations 172 (Table S3)), thus validating the accuracy of this approach. 173 To provide a pseudo 3D metric, CCV curvature, the average grey value of the CCV was used as 174 a proxy for how spherical/3D the CCV is. During its development, the CCV becomes more protein 175 dense and projects away from the PM, yielding a higher grey value than flat structures on the PM (4) 176 ( Fig. 3C). By normalizing the CCV grey values to an area of flat PM in each image, we can generate 177 relative estimates of 3D curvature and compare them across different images. Comparing the 178 curvature estimation from the manual and automated segmentations from the validation wild-type 179 data resulted in similar values; 2.23 ± 0.07 arbitrary units (au) for manual and 2.19 ± 0.07 au for 180 automated segmentations (Table S3). 181 To highlight the robustness of this protocol in identifying and screening the 'pseudo 3D' 182 morphology of CCVs in a high throughput manner, we applied it to SEM images of unroofed cells where 183 the clathrin-mediated endocytosis membrane bending machinery was disrupted (Fig. 4A). These 184 images were of metal replicas of unroofed protoplasts derived from the roots of WDXM2 seedlings, 185 an inducible loss-of-function TPLATE mutant that prevents the formation of spherical CCVs (6, 25), 186 subjected to control (non-induction) and disruptive (induced) conditions. The automated 187 segmentation successfully detected the clathrin structures during both experimental conditions, 188 finding one additional CCV compared to manual segmentation for each condition. The IoU pixel 189 overlap was 92% when comparing the manual and automated segmentations (Table S4) To further define these morphological differences, we imposed thresholds upon the data (4). First, an 194 area threshold of 8500 nm 2 (diameter of ~105 nm) was used to classify clathrin structures as 'large' or 195 'small'. To classify clathrin structures based on their 3D shape, we identified 'curved' or 'flat' Understanding the structural details of biological samples provides key insights into their functions, 212 therefore high-resolution 3D imaging is an extremely powerful tool for investigating cell biology. While 213 EM techniques are routinely used to achieve this, they are traditionally often reliant upon harsh 214 and/or dangerous chemicals requiring specialized facilities to conduct them (5, 6), which can limit their 215 accessibility to the biological community. To overcome this, we developed protocols which allow the 216 3D examination of metal replicated samples in any routine EM lab (Fig. 5). Specifically, we established 217 a method for preparing samples (unroofed cells and biochemically isolated organelles) directly on EM 218 grids, providing a standardized sample preparation protocol which does not require the use of 219 extremely toxic acids. These preparations can be combined with STEM tomography to produce 220 nanometer resolution 3D images. However, as this high-resolution imaging can be time consuming, 221 we also developed a high throughput deep learning-based analysis which provides a protocol for rapid 222 morphological 'pseudo 3D' screening of biological structures. While these protocols can likely be 223 adjusted for a range of cell types and organelles, here we applied them to further characterize the 3D 224 morphology of plant CCVs -an essential trafficking organelles underpinning many cellular processes 225 (26) -at high resolutions. We first visualized the clathrin assembly of CCVs in unroofed protoplasts to 226 investigate endocytic CCV structures. By using the same sample preparation for biochemically isolated 227 CCVs, we could directly compare them to specific CCV populations visualized inside cells. While we 228 found that there were small differences in the sizes of endocytic and isolated CCVs, this is likely due 229 to the fact that the isolated CCV preparations from whole cell lysates contain a mixture of CCVs 230 derived from different origins -clathrin-mediated endocytosis and the early endosome/trans-Golgi 231 network (22,27,28). We then validated our high throughput pseudo 3D analysis by applying it to 232 metal replicas of unroofed cells carrying an inducible loss of endocytic membrane bending. 233 To produce samples which reliably contain planta CCVs, we made use of protoplasts. This is 234 because for an unknown reason, CCVs are rarely preserved no matter which EM sample preparation 235 is used with whole plant samples. Therefore, to produce any robust quantitative analysis we had to 236 utilize a cell system which allowed the consistent preservation of CCVs. As a result, we are examining 237 plant CCVs which have formed in a different biophysical cellular environment compared to those in 238 intact plants. However, protoplasts contain the same protein machinery responsible for producing 239 CCVs, and the diameters of the protoplast CCVs (76 nm) are similar to the few reported measurements 240 made in intact plant tissues (~80 nm (17, 18)), thus highlighting that the difference in cellular 241 environments does not affect the overall production of CCVs. 242 We focused on utilizing protoplasts derived directly from Arabidopsis seedings. This allows 243 one to use already existing and established genetically altered plants as starting material. 244 Furthermore, it provides the opportunity to reliably examine the effect of mutants upon CCV 245 formation and structure. The use of homozygous Arabidopsis seedlings ensures that every cell 246 unroofed carries the genetic manipulation, which is not possible to guarantee using transient 247 transfection protocols which are not 100% efficient. 248 Our high-throughput screening of CCVs pseudo 3D morphology analysis was developed using 249 samples attached to glass coverslips. This demonstrates that 3D analysis of samples can be conducted 250 even without high end STEM enabled microscopes. To test the versatility of this analysis we applied 251 the same CCV segmentation model to SEM images of metal replicas of unroofed protoplasts prepared 252 directly on EM grids (Protocol 1) and to isolated CCVs (Protocol 2) (Fig. S3). We found that the model 253 was accurate for both the in cell and purified CCVs. However, to further improve this accuracy, we 254 suggest training specialized models using representative CCV images of the specific preparation 255 method of interest. 256 We also expect our protocols to become a platform for further technical innovations and 257 optimizations to continue advancing our understanding of these critical trafficking organelles. For 258 example, combining these methods with immunolabeling approaches and fluorescence super 259 resolution microscopy, such as correlated light and electron microscopy (CLEM) procedures, would 260 help to unravel the molecular composition of CCVs and allow the precise localization of CCV related 261 proteins. These sample preparation methods could also aid researchers to further develop focused 262 ion beam (FIB) milling approaches to capture images of CCV in situ inside plant tissues. 263 Overall, we present refined accessible user-friendly methods for the 3D analysis of biological 264 samples and investigate the 3D structural details of plant CCVs at unprecedented resolutions and 265 accuracy. The experimental workflows provide the opportunity for the direct comparison of CCVs 266 derived from differing sources, like specific cell types and purified CCV preparations. Additionally, the 267 methods allow one to define individual structures at high resolution and screen CCVs in a high 268 throughput machine learning assisted analysis to robustly quantify their morphology. 269 pH5.5 adjusted with KOH) and plated on to pre-prepared EM grids (detailed in the following section), 290

Materials and Methods 270
where they were incubated in a humid chamber for 3 hours. The cells were then washed with PBS. To 291 unroof the cells, they were then incubated with extraction buffer (2 μM phalloidin, 2 μM taxol, 1% 292 (w/v) Triton X-100 and 1% (w/v) polyethylene glycol (PEG; MW 2,000) in PEM buffer (100 mM PIPES 293 free acid, 1 mM MgCl2, 1 mM EGTA; pH 6.9 adjusted with KOH) for 5 minutes with gentle agitation. 294 The samples were then washed three times with PEM buffer supplemented with 1% (w/v) PEG 2,000. Purified CCVs samples were isolated from undifferentiated T87 Arabidopsis suspension cell 299 cultures by differential gradient centrifugation, as previously described (22). 5 µl of the CCV 300 preparation ([0.33 mg/ml]) was plated and incubated on a pre-prepared EM grid (detailed in the 301 following section) for minutes. Whatman TM blotting paper was used to remove the excess solution. 302 The samples were then fixed by a 30-minute incubation with 2% GA in 0.1 M PB via a 30-minute 303 incubation. 304

EM grid preparations 305
Gold EM grids with Formvar-film (Electron Microscopy Sciences #G300PB-Au) were used for 306 processing protoplast samples. They were coated with carbon to a thickness of 10 nm using a Leica 307 ACE600 coating device. The grids were then supplemented with poly-l-lysine (Sigma) by a 15-minute 308 incubation and washed with H2O. 309 Copper EM grids with carbon-film (Electron Microscopy Sciences #CF300-Cu) were used for 310 isolated CCV preparations. They were subjected to glow discharge (4 min at 7x10E-1 mbar) using the 311 ELMO glow discharge system (Agar Scientific Ltd.). 312

Metal replication of samples 313
The fixed samples were washed with 0.1 M PB and H2O, and then incubated with 0.1% (w/v) tannic 314 acid for 20 minutes. The samples were washed three times with H2O and incubated with 0.2% (w/v) 315 uranyl acetate for 20 minutes. To dehydrate the samples, they were washed three times with H2O, 316 infiltrated with graded ethanol (10%, 20%, 40%, 60%, 80%, 96% and 100%) and subjected to a 2-317 minute incubation with hexamethyldisilane. The samples were then dried by evaporation. The 318 samples were then coated with 3 nm platinum and 4 nm carbon using the ACE600 coating device 319 (Leica Microsystems). 320 STEM tomography 321 STEM tomograms were acquired with a JEOL JEM2800 scanning-transmission electron microscope 322 (200 kV AV) control by STEM Recorder (https://temography.com/en/). Diameters of the CCVs were 323 determined from the 0° single plane STEM image, where an average value was calculated from the 324 maximum and minimum Feret distances of a ROI drawn around the CCV in Fiji (29). This value was 325 then used to calculate a spherical volume. Tomograms of single CCVs were recorded over a range of 326 ~-72° to +72° along single tilt axis with step size 4°. They were aligned and reconstructed using 327 Composer (https://temography.com/en/). Wireframe models of the CCVs were manually created 328 using Evo-viewer (https://temography.com/en/), which were used to quantify aspects of the CCV 329 structure. Data was collected from at least 2 repeats. Reported quantitative measurement values in 330 the main text are reported as mean ± SEM. 331 High throughput pseudo 3D morphology screening of CCVs 332 SEM images of metal replicas of unroofed protoplasts, taken with a FE-SEM Merlin Compact VP 333 equipped with an In-lens Duo detector, were obtained from Johnson et al (6). This included images of 334 CCVs in Col-0 and WDMX2 protoplasts incubated in control (4-hour incubation at room temperature) 335 or endocytic membrane bending disruptive (4-hour incubation at 37°C) conditions, which were 336 collected using the same magnification (~45 k) and pixel size settings (~2.4-2.6 nm). These protoplasts 337 were prepared on glass coverslips as previously described (6). 338 For the automated segmentation of CCV, we first generated a training set consisting of 13 339 example image pairs (raw image and segmentation masks) of the CCVs in replica images of wild type 340 and WDXM2 cells subjected to control or disruptive conditions. CCVs in the raw images were manually 341 annotated using the Napari software (30). We then trained Cellpose (23) using input for 5000 epochs 342 (see Protocol 1 for further details). The resulting trained model was then used to segment individual 343 CCVs. To determine the localization accuracy of the automated CCV segmentation, we calculated the 344 mean pixel-wise Intersection over Union (IoU) of manually annotated and predicted CCVs. Pixel-wise 345 IoU is defined by the number of pixels in both, the and the CCV segmentation 346 mask, over the number of pixels of the union of both segments. 347 The predicted segmentation ROIs were then exported to Fiji (29) to quantify the area and average grey 349 value of each CCV. Diameters of the equivalent circle were calculated from the CCV area. To estimate 350 the curvature/pseudo 3D of the CCV, the average grey value of the CCV ROI was normalized by division 351 to the average grey value of 4 manually selected PM ROIs in each image. The area and pseudo 3D 352 values were plotted to define the overall morphology of a CCV. This was then divided in to 4 353 morphological categories (SR, small and round; SF, small and flat; LR, large and round; LF, large and 354 flat) using an area threshold of 8500 nm 2 (a CCV diameter of 105 nm) and a 3D value of 1.52 (the 355 average of the 3 smallest CCVs in control conditions determined to be spherical by the experimenter). 356

Data Availability 357
Example data and the code generated in this study is available at: Protocol 1 -STEM Tomography of unroofed protoplast cells derived from Arabidopsis seedlings; 417 magenta, Protocol 2 -STEM Tomography of isolated CCVs derived from cultured Arabidopsis cells; 418 green, Protocol 3 -high throughput pseudo 3D morphological screening of CCVs). 419 420