Selecting particles from digital micrographs is an essential step in single-particle electron cryomicroscopy (cryo-EM). As manual selection of complete datasets—typically comprising thousands of particles—is a tedious and time-consuming process, numerous automatic particle pickers have been developed. However, non-ideal datasets pose a challenge to particle picking. Here we present the particle picking software crYOLO which is based on the deep-learning object detection system You Only Look Once (YOLO). After training the network with 200–2500 particles per dataset it automatically recognizes particles with high recall and precision while reaching a speed of up to five micrographs per second. Further, we present a general crYOLO network able to pick from previously unseen datasets, allowing for completely automated on-the-fly cryo-EM data preprocessing during data acquisition. crYOLO is available as a standalone program under http://sphire.mpg.de/ and is distributed as part of the image processing workflow in SPHIRE.

Selecting particles from digital micrographs is an essential step in single particle electron cryomicroscopy (cryo-EM). Since manual selection of complete datasets typically comprising many thousands of particles is a tedious and time-consuming process, many automatic particle pickers have been developed in the past few decades.However, non-ideal datasets pose a challenge to particle picking. Here, we present a novel automated particle picking software called crYOLO, which is based on the deep learning object detection system "You Only Look Once" (YOLO). After training the network with 500 -2,500 particles per dataset, it automatically recognizes particles with high recall and precision reaching a speed of up to five micrographs per second.Importantly, we demonstrate a powerful general network trained on more than 40 datasets to select previously unseen datasets, thus paving the way for completely automated "on-the-fly" cryo-EM data pre-processing during data acquisition. CrYOLO is available as a standalone program under http://sphire.mpg.de/ and will be part of the image processing workflow in SPHIRE.

SPHIRE (SPARX for High-Resolution Electron Microscopy) is a novel open-source, user-friendly software suite for the semi-automated processing of single particle electron cryo-microscopy (cryo-EM) data. The protocol presented here describes in detail how to obtain a near-atomic resolution structure starting from cryo-EM micrograph movies by guiding users through all steps of the single particle structure determination pipeline. These steps are controlled from the new SPHIRE graphical user interface and require minimum user intervention. Using this protocol, a 3.5 Å structure of TcdA1, a Tc toxin complex from Photorhabdus luminescens, was derived from only 9500 single particles. This streamlined approach will help novice users without extensive processing experience and a priori structural information, to obtain noise-free and unbiased atomic models of their purified macromolecular complexes in their native state.

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All known triterpenes are generated by triterpene synthases (TrTSs) from squalene or oxidosqualene1. This approach is fundamentally different from the biosynthesis of short-chain (C10–C25) terpenes that are formed from polyisoprenyl diphosphates2–4. In this study, two fungal chimeric class I TrTSs, Talaromyces verruculosus talaropentaene synthase (TvTS) and Macrophomina phaseolina macrophomene synthase (MpMS), were characterized. Both enzymes use dimethylallyl diphosphate and isopentenyl diphosphate or hexaprenyl diphosphate as substrates, representing the first examples, to our knowledge, of non-squalene-dependent triterpene biosynthesis. The cyclization mechanisms of TvTS and MpMS and the absolute configurations of their products were investigated in isotopic labelling experiments. Structural analyses of the terpene cyclase domain of TvTS and full-length MpMS provide detailed insights into their catalytic mechanisms. An AlphaFold2-based screening platform was developed to mine a third TrTS, Colletotrichum gloeosporioides colleterpenol synthase (CgCS). Our findings identify a new enzymatic mechanism for the biosynthesis of triterpenes and enhance understanding of terpene biosynthesis in nature.

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A high-resolution positron emission tomography (PET) scanner, dedicated to brain studies, was developed and its performance was evaluated. A four-layer depth of interaction detector was designed containing five detector units axially lined up per layer board. Each of the detector units consists of a finely segmented (1.2 mm) LYSO scintillator array and an 8  ×  8 array of multi-pixel photon counters. Each detector layer has independent front-end and signal processing circuits, and the four detector layers are assembled as a detector module. The new scanner was designed to form a detector ring of 430 mm diameter with 32 detector modules and 168 detector rings with a 1.2 mm pitch. The total crystal number is 655 360. The transaxial and axial field of views (FOVs) are 330 mm in diameter and 201.6 mm, respectively, which are sufficient to measure a whole human brain. The single-event data generated at each detector module were transferred to the data acquisition servers through optical fiber cables. The single-event data from all detector modules were merged and processed to create coincidence event data in on-the-fly software in the data acquisition servers. For image reconstruction, the high-resolution mode (HR-mode) used a 1.2 mm crystal segment size and the high-speed mode (HS-mode) used a 4.8 mm size by collecting 16 crystal segments of 1.2 mm each to reduce the computational cost. The performance of the brain PET scanner was evaluated. For the intrinsic spatial resolution of the detector module, coincidence response functions of the detector module pair, which faced each other at various angles, were measured by scanning a 0.25 mm diameter Na point source. The intrinsic resolutions were obtained with 1.08 mm full width at half-maximum (FWHM) and 1.25 mm FWHM on average at 0 and 22.5 degrees in the first layer pair, respectively. The system spatial resolutions were less than 1.0 mm FWHM throughout the whole FOV, using a list-mode dynamic RAMLA (LM-DRAMA). The system sensitivity was 21.4 cps kBq as measured using an F line source aligned with the center of the transaxial FOV. High count rate capability was evaluated using a cylindrical phantom (20 cm diameter  ×  70 cm length), resulting in 249 kcps in true and 27.9 kcps at 11.9 kBq ml at the peak count in a noise equivalent count rate (NECR_2R). Single-event data acquisition and on-the-fly software coincidence detection performed well, exceeding 25 Mcps and 2.3 Mcps for single and coincidence count rates, respectively. Using phantom studies, we also demonstrated its imaging capabilities by means of a 3D Hoffman brain phantom and an ultra-micro hot-spot phantom. The images obtained were of acceptable quality for high-resolution determination. As clinical and pre-clinical studies, we imaged brains of a human and of small animals.

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