Convolutional neural networks for automated annotation of cellular cryo-electron tomograms

Abstract

Cellular electron cryotomography offers researchers the ability to observe macromolecules frozen in action in situ, but a primary challenge with this technique is identifying molecular components within the crowded cellular environment. We introduce a method that uses neural networks to dramatically reduce the time and human effort required for subcellular annotation and feature extraction. Subsequent subtomogram classification and averaging yield in situ structures of molecular components of interest. The method is available in the EMAN2.2 software package.

Fig 1. Workflow of tomogram annotation, using a PC12 cell as an example

a. Slice view of a raw tomogram input. Locations of representative positive (white) and negative (black) examples are shown as boxes. b. Training and annotation of a single feature. Representative 2D patches of both positive (10 total) and negative examples (86 total) are extracted and manually segmented. Time required for manual selection/annotation was ~5minutes. Tomogram patches and their corresponding annotations are used to train the neural network, so that the network outputs match manual annotations. The trained neural network is applied to whole 2D slices and the feature of interest is segmented. c. Annotation of multiple features in a tomogram. Four neural networks are trained independently to recognize double membrane (yellow), single membrane (blue), microtubule (cyan) and ribosome (pink). d. Masked out density of the merged final annotation of the four features.

Fig 2. Results from tomogram annotation

a–c. Masked out density of tomogram annotation results. a.Trypanosome. Purple: membrane; cyan: microtubules; pink: ribosomes. b. Cyanobacteria. Purple: vertical membranes; pink: ribosomes; red: phages; white: RubisCOs; dark blue: carboxysomes; green: proteins on thylakoid membrane. c. Platelet. Purple: membranes with similar intensity value on both side; orange: membranes of vesicles with darker density inside; cyan: microtubules. d–f. Subtomogram averaging of extracted particles. d. Horizontal thylakoid membrane in cyanobacteria. Top-left: 2D class average of particle projections; bottom-left: FFT of 2D class average; top-right: top view of 3D average; bottom-right: side view of 3D average. e. Microtubules. Top-left: in vitro microtubule structure (EMDB 8095), lowpass filtered to 40A; top-right: average of trypanosome microtubules (N=511). Density on the left and right resembles adjacent microtubules; middle-left: FFT of projection of the EMDB structure; middle-right: Incoherent average of FFT of projection of trypanosome microtubule; bottom-left: average of PC12 microtubules (N=677); bottom-right: average of platelet microtubules (N=312). f. Ribosomes. Left: in vitro ribosome structure (EMDB 2239), lowpass filtered to 40Å; right: average of trypanosome ribosomes (N=759);