2dx_automator: implementation of a semiautomatic high-throughput high-resolution cryo-electron crystallography pipeline.

The introduction of direct electron detectors (DED) to cryo-electron microscopy has tremendously increased the signal-to-noise ratio (SNR) and quality of the recorded images. We discuss the optimal use of DEDs for cryo-electron crystallography, introduce a new automatic image processing pipeline, and demonstrate the vast improvement in the resolution achieved by the use of both together, especially for highly tilted samples. The new processing pipeline (now included in the software package 2dx) exploits the high SNR and frame readout frequency of DEDs to automatically correct for beam-induced sample movement, and reliably processes individual crystal images without human interaction as data are being acquired. A new graphical user interface (GUI) condenses all information required for quality assessment in one window, allowing the imaging conditions to be verified and adjusted during the data collection session. With this new pipeline an automatically generated unit cell projection map of each recorded 2D crystal is available less than 5 min after the image was recorded. The entire processing procedure yielded a three-dimensional reconstruction of the 2D-crystallized ion-channel membrane protein MloK1 with a much-improved resolution of 5Å in-plane and 7Å in the z-direction, within 2 days of data acquisition and simultaneous processing. The results obtained are superior to those delivered by conventional photographic film-based methodology of the same sample, and demonstrate the importance of drift-correction.

SynapseCLR: Uncovering features of synapses in primary visual cortex through contrastive representation learning.

3D electron microscopy (EM) connectomics image volumes are surpassing 1 mm3, providing information-dense, multi-scale visualizations of brain circuitry and necessitating scalable analysis techniques. We present SynapseCLR, a self-supervised contrastive learning method for 3D EM data, and use it to extract features of synapses from mouse visual cortex. SynapseCLR feature representations separate synapses by appearance and functionally important structural annotations. We demonstrate SynapseCLR's utility for valuable downstream tasks, including one-shot identification of defective synapse segmentations, dataset-wide similarity-based querying, and accurate imputation of annotations for unlabeled synapses, using manual annotation of only 0.2% of the dataset's synapses. In particular, excitatory versus inhibitory neuronal types can be assigned with >99.8% accuracy to individual synapses and highly truncated neurites, enabling neurite-enhanced connectomics analysis. Finally, we present a data-driven, unsupervised study of synaptic structural variation on the representation manifold, revealing its intrinsic axes of variation and showing that representations contain inhibitory subtype information.

A high-throughput electron tomography workflow reveals over-elongated centrioles in relapsed/refractory multiple myeloma.

Electron microscopy is the gold standard to characterize centrosomal ultrastructure. However, production of significant morphometrical data is highly limited by acquisition time. We therefore developed a generalizable, semi-automated high-throughput electron tomography strategy to study centrosome aberrations in sparse patient-derived cancer cells at nanoscale. As proof of principle, we present electron tomography data on 455 centrioles of CD138pos plasma cells from one patient with relapsed/refractory multiple myeloma and CD138neg bone marrow mononuclear cells from three healthy donors as a control. Plasma cells from the myeloma patient displayed 122 over-elongated centrioles (48.8%). Particularly mother centrioles also harbored gross structural abnormalities, including fragmentation and disturbed microtubule cylinder formation, while control centrioles were phenotypically unremarkable. These data demonstrate the feasibility of our scalable high-throughput electron tomography strategy to study structural centrosome aberrations in primary tumor cells. Moreover, our electron tomography workflow and data provide a resource for the characterization of cell organelles beyond centrosomes.