Software tools for automated transmission electron microscopy


The demand for high-throughput data collection in electron microscopy is increasing for applications in structural and cellular biology. Here we present a combination of software tools that enable automated acquisition guided by image analysis for a variety of transmission electron microscopy acquisition schemes. SerialEM controls microscopes and detectors and can trigger automated tasks at multiple positions with high flexibility. Py-EM interfaces with SerialEM to enact specimen-specific image-analysis pipelines that enable feedback microscopy. As example applications, we demonstrate dose reduction in cryo-electron microscopy experiments, fully automated acquisition of every cell in a plastic section and automated targeting on serial sections for 3D volume imaging across multiple grids.

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Fig. 1: Automatic generation of virtual maps used to acquire TORC1 filamentous particles with cryo-EM.
Fig. 2: Illustration of the workflow used to automatically acquire all cells on a resin section.
Fig. 3: Automated acquisition of cells on serial sections.

Data availability

All raw data presented in the ‘Applications’ section of this paper are available from the corresponding authors upon reasonable request.


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We thank A. Krämer for initiating and coordinating the scientific project on leukocytes. We thank A. Desfosses and M. Prouteau for providing specimens for testing and application of the cryo-EM workflow. We acknowledge support from C. Palmer for mrcfile, and C. Dietz and C. von Schwerin for KNIME Image Processing and Python bindings. We thank all staff of the EM Core Facility at EMBL for helpful discussions and ideas. We thank R. Mellwig for critical reading of and comments on the manuscript. We also acknowledge support from the EMBL Center of Bioimage Analysis (CBA). I.H. is the recipient of an HRCMM (Heidelberg Research Center for Molecular Medicine) Career Development Fellowship. Work on SerialEM was supported originally by grants from NIH and more recently by contributions from users and from JEOL USA, Inc., as well as by payments for specific projects by Hitachi High Technologies America, Inc., JEOL USA, Inc., and Direct Electron, LP.

Author information




M.S., I.H., W.J.H.H. and Y.S. designed the experimental applications. M.S. and D.N.M. did the software development. I.H., W.J.H.H. and M.S. collected the data. All authors contributed valuable suggestions on the necessary software functionality and edited the article. M.S. and D.N.M. wrote the initial manuscript.

Corresponding authors

Correspondence to Martin Schorb or David N. Mastronarde.

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The authors declare no competing interests.

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Integrated supplementary information

Supplementary Figure 1 The KNIME workflow used for detecting each cell on a resin section (Application 2).

a - the main KNIME workflow with nodes and data connectors. Red connections deliver parameters, grey connections indicate data (images, metadata) handover. Dark blue boxes show the configuration dialogs where the user provides the location of the Navigator file and some parameters for image analysis. The user can monitor the result of the segmentation using the "Interactive Segmentation View" or manually correct misplaced labels using the "Interactive Labeling Editor". The initial Python nodes shown on the bottom left use py-EM to parse the Navigator file and import the map image(s). If desired, an IMOD model can be drawn that will act as a mask defining the image area for processing. The image analysis meta-node contains the entire procedure that extracts the position and outline of each individual cell from the image. Its detailed contents are depicted in b. The "Interactive Labeling Editor" node enables manual curation of the detected cells. The final Python scripting node generates the Navigator file and fills it with a Virtual Map and the polygon outline for each cell. b - The KNIME image analysis pipeline executed within the meta-node shown in a. Each node performs an individual processing task. The respective result is shown above for reference. Some nodes incorporate external functionality from Python or ImageJ. The output is an image with a distinct pixel intensity value ("label") for each detected cell. The automated identification of cells presented in this figure has been successfully applied to 26 sections for this experiment and with modified image analysis pipelines for two different specimens.

Supplementary Information

Supplementary Information

Supplementary Figure 1

Reporting Summary

Supplementary Protocol 1

High-yield automated cryo-EM data acquisition of large particles

Supplementary Protocol 2

Automated serial-section TEM

Supplementary Video 1

Every detected cell on the section. An image stack with one overview image for each of the automatically detected 1,325 cells. Scale bar, 1 µm. Automated identification of cells as shown in this video has been successfully applied to 26 sections with about 1,000 cells each for this experiment, and with modified image-analysis pipelines for two different specimens

Supplementary Video 2

One cell across nine grids. Series of map images of a single cell of interest across 42 serial sections on 9 consecutive grids. The centriole is marked with a blue arrow. Scale bar, 1 µm. In the presented experiment, we followed a total of 120 cells across 100 sections on 20 grids for 3 different specimens

Supplementary Video 3

How to register serial sections. Video tutorial of the registration procedure from one section to the next. It demonstrates how to propagate the maps of 50 cells and relocate them on the next section

Supplementary Video 4

Reconstruction of serial tomograms of a centriole. Reconstruction of serial tomograms of the centriole region marked in SM1. The acquisition area spans six sections covering approximately 1.2 µm. Scale bar, 200 nm. In the presented experiment, we acquired a total of 992 tomograms in series on consecutive sections for 120 cells

Supplementary Software

Py-EM and SerialEM software files

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Schorb, M., Haberbosch, I., Hagen, W.J.H. et al. Software tools for automated transmission electron microscopy. Nat Methods 16, 471–477 (2019).

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