Cryogenic electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. However, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present DeepFinder, a computational procedure that uses artificial neural networks to simultaneously localize multiple classes of macromolecules. Once trained, the inference stage of DeepFinder is faster than template matching and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (roughly 3.2 MDa), ribulose 1,5-bisphosphate carboxylase–oxygenase (roughly 560 kDa soluble complex) and photosystem II (roughly 550 kDa membrane complex) with an accuracy comparable to expert-supervised ground truth annotations. DeepFinder is therefore a promising algorithm for the semiautomated analysis of a wide range of molecular targets in cellular tomograms.
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The synthetic dataset (Dataset 1) is available on the website of the SHREC 2019 challenge (http://www2.projects.science.uu.nl/shrec/cryo-et/2019/). A tomogram from the experimental dataset of C. reinhardtii cells (Dataset 2)34,60 can be found in the EMDB under accession number EMD-3967 (ref. 61). The test tomogram of the Chlamydomonas pyrenoid used for subtomogram averaging (Dataset 3)37 can be downloaded from the EMDB under accession number EMD-12749, and the raw tilt series data for this tomogram are available at the Electron Microscopy Public Image Archive (EMPIAR) under accession number EMPIAR-10694. All four tomograms used to train and test the detection of PSII in Chlamydomonas thylakoids (Dataset 4)38 can be downloaded from the EMDB under accession numbers EMD-10780, EMD-10781, EMD-10782 and EMD-10783.
The code can be downloaded for free from our GitLab website (https://gitlab.inria.fr/serpico/deep-finder) along with accompanying documentation (https://deepfinder.readthedocs.io/en/latest/). DeepFinder is embedded into the new release of Scipion62 (https://github.com/scipion-em/scipion-em-deepfinder), an open-source image processing framework for cryo-electron microscopy (http://scipion.i2pc.es/).
Each step of DeepFinder shown in Fig. 1a can be executed with scripts using the API (examples are provided) or with a GPU. These steps may also be embedded in other workflows, for example, if the user needs only the segmentation step. To implement DeepFinder, we used Keras (http://keras.io), an open-source toolbox written in Python and using the TensorFlow framework.
All training procedures were achieved using a Nvidia Tesla K80 GPU, running Cuda 8 and cuDNN 6. In the code, we display the memory consumption of DeepFinder for different training parameters.
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This work was jointly supported by the Fourmentin-Guilbert Foundation and Région Bretagne (Brittany Council). Calculations were performed on the Inria Rennes computing grid facilities partly funded by France-BioImaging infrastructure (French National Research Agency—ANR-10-INBS-04-07, ‘Investments for the future’) and at the Max Planck Institute for Biochemistry computing cluster, Martinsried, Germany. L.L., R.D.R., W.W., T.P. and B.D.E. were supported by DFG grant no. EN 1194/1-1 as part of FOR 2092, The Munich School for Data Science (MUDS) and Helmholtz Association. A.M.-S. was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy EXC 2067/1-390729940.
We thank F. Förster and M. Killinger for fruitful discussions about cryo-ET data analysis and deep learning applied to large 3D volumes analysis, respectively.
We thank the organizers of the SHREC 2019 and SHREC 2020 challenges for helpful assistance and for providing the template matching results: I. Gubins and R.C. Veltkamp (Utrecht University, Department of Information and Computing Sciences), G. van der Schot and F. Förster (Utrecht University, Department of Chemistry).
Finally, we thank S. Prima for careful reading of the paper and valuable suggestions and comments.
The authors declare no competing interests.
Peer review information Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
a, Conventional processing pipeline based on template matching. b, DeepFinder (analysis stage): a multi-class approach able to localize particles of several different macromolecular species in one pass. a, and b, highlight why DeepFinder is more agile than template matching when several macromolecule classes need to be localized. c, CNN architecture used in DeepFinder and based on U-Net27. The architecture adopts the encoder-decoder paradigm, which produces an output volume with the same size as the input volume. Each green box represents a convolutional layer. The number of filters n and the filter size s is labeled as n × (s × s × s). All convolutional layers are followed by a ReLU activation function, except the last layer, which uses a soft-max function. The up-sampling is achieved with up-convolutions (also called ‘backward-convolution’). Combining feature maps from different scales is performed by concatenation along the channel dimension. In the end, the total number of architecture parameters is approximately 903k. More precisely, this number depends slightly on Ncl, the number of classes: 902, 928 + Ncl × 33.
a, Training interface composed of a first window for parametrizing the procedure and a second window for displaying the training metrics in real-time. b, Segmentation interface which also opens a data visualization tool. This tool allows the user to explore the tomogram with superimposed segmentations. In addition, DeepFinder also incorporates interfaces for tomogram annotation, target generation and clustering (see the documentation at https://gitlab.inria.fr/serpico/deep-finder for more information).
Extended Data Fig. 3 Analysis of algorithm performance on the synthetic dataset (SHREC’20 challenge).
a, Performance (F1-score) of DeepFinder, UMC and template matching algorithms and ability of algorithms to discriminate between 12 classes/subclasses of macromolecules. The highest (best) possible value of an F1-score is 1.0 and the lowest (worst) possible value is 0. The scores of template matching were provided by the SHREC’20 challenge organizers (Utrecht University, Department of Information and Computing Sciences and Department of Chemistry). b, Performance of DeepFinder implemented as a multi-class network architecture and as an architecture made of 12 binary networks. These two architectures differ only by the number of output neurons. c, Influence of the training target generation method (‘shapes’ versus ‘spheres’). In the case of ‘shapes’, the exact shapes of the macromolecules have been used to annotate the tomograms. In the case of ‘spheres’, the shape and the orientation of macromolecules are not needed to generate the training targets. This analysis used eight tomograms for training, one tomogram for validation, and one tomogram for testing.
Extended Data Fig. 4 Evolution of F1-scores with respect to sizes of the training sets (number of tomograms) on the synthetic SHREC dataset (12 classes).
Scores are displayed for both the SHREC 2019 a, and 2020 b, editions. This figure gives an estimation of the amount of annotated data needed to identify macromolecules. This amount depends on the size of the target macromolecule: smaller targets require more annotations. Each tomogram contains in average 208 macromolecules per class. The macromolecules have been categorized into four groups (large, medium, small and tiny). This analysis used eight tomograms for training, one tomogram for validation, and one tomogram for testing.
Extended Data Fig. 5 Evolution of F1-score with respect to training iterations and training set size on real cryo-ET Dataset #2, Chlamydomonas reinhardtii (3 classes).
a, The loss, which quantifies the segmentation quality, is computed for the training set, as well as for the validation set. Comparing both curves allows assessment of the generalization capabilities of DeepFinder. The curves for both sets should ideally overlap, otherwise it indicates overfitting (the network memorizes trained samples instead of learning discriminating features). One epoch equals 100 training iterations. b, The F1-score, which quantifies the localization performance, computed on the test set. The F1-score is obtained by comparing the membrane-bound ribosomes found by DeepFinder to expert annotations. The time axis has been obtained using a Tesla K80 GPU. The curve indicates that competitive particle picking results are obtained after 20 epochs, or 4.3 hours with the required GPU. This analysis used 21 tomograms for training, one tomogram for validation, and eight tomograms for testing. c, In a similar fashion to Extended Data Fig. 4, this curve provides an estimate of the quantity of training data required to achieve a competitive result. It appears that this quantity is 1,400 ribosomes (nine tomograms), which is a typical size for a cryo-ET dataset. On first glance, this estimate seems to contradict the estimates in Extended Data Fig. 4: the numbers do not coincide (the curve labeled ‘Large’ estimates that quantity at 208 particles). Note that SHREC’19 is a synthetic dataset, composed of 12 classes. Here, we are dealing with a real cellular dataset consisting of three classes (membrane, mb-ribo and ct-ribo). It appears that having a larger number of classes enables the use of smaller training sets. On the other hand, the case of real data is more difficult, notably because of the presence of ‘label noise’ (errors due to the annotation pipeline) and other sources of signal corruption such as the missing wedge, the contrast transfer function and the low signal-to-noise ratio (in part caused by increased molecular crowding inside cells). This analysis used one tomogram for validation, and eight tomograms for testing.
Extended Data Fig. 6 Quantitative analysis of overlap with expert annotations on cellular cryo-ET data (Dataset #2, mb-ribos).
We varied the thresholds of template matching (a) and DeepFinder (b) to compute the Recall (ratio between the number of true positives and the number of particles in the ground truth), Precision (ratio between the number of true positives and the number of detected particles) and F1-score (2 × (Recall × Precision) / (Recall + Precision)) curves. The threshold parameter for template matching is the constrained correlation coefficient, and for DeepFinder it is the cluster size, which corresponds to the macromolecule volume (in voxels). We obtained a maximum F1-score of 0.86 for DeepFinder and a maximum F1-score of 0.50 for template matching (with no post-classification step, see Extended Data Fig. 1a). Template matching and DeepFinder both have good Recall values, but template matching has a lower Precision than DeepFinder. This suggests that template matching can be recommended to select many candidates, but a time-consuming post-classification is required to improve Precision. DeepFinder has much higher Precision values, which confirms the results from the synthetic dataset (SHREC’19 challenge). This analysis used 48 tomograms for training, one tomogram for validation, and eight tomograms for testing.
a, Tomogram slice depicting the border of a FIB-milled lamella. The lamella contains a Chlamydomonas reinhardtii cell, with a lamella surface suffering from ice contamination. b, Tomogram slice with superimposed DeepFinder segmentation. Most of the ice contamination artifacts have been correctly classified as ‘background’. Nonetheless, some missclassifications exist, as can be observed in the zoomed-in boxes (in dashed red). In boxes 1 and 2, DeepFinder confuses some artifacts with membranes, and some features are wrongly classified as membrane-bound ribosomes. Such missclassifications can be filtered out, either by masking the boundaries of the lamella, or by rejecting segmented objects that are too small (using the ‘cluster size’ attribute given by the clustering step of the DeepFinder analysis stage). This analysis used 48 tomograms for training, one tomogram for validation, and eight tomograms for testing.
DeepFinder was trained on the Chlamydomonas (algae) dataset and then applied on a tomogram of mouse P19 cells (EMD-10439). Although the ribosome has a different structure for the two species, for a given voxel size (13.68 Å) the structures are similar enough for DeepFinder to identify and localize mb-ribo particles in a P19 cell. a, Tomographic slice with both the superimposed segmented cell membrane (gray) and mb-ribo particles (blue). b, Average density from 300 mb-ribo particles. c, Histogram of mb-ribo particle distance from the nearest cell membrane. In this histogram, the maximum mode is located at 136.8 Å, which corresponds to the ribosome radius. This analysis used 48 tomograms for training, one tomogram for validation, and one tomogram for testing.
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Moebel, E., Martinez-Sanchez, A., Lamm, L. et al. Deep learning improves macromolecule identification in 3D cellular cryo-electron tomograms. Nat Methods 18, 1386–1394 (2021). https://doi.org/10.1038/s41592-021-01275-4
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