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Cryo-electron tomography (cryo-ET) allows for label-free high-resolution imaging of macromolecular assemblies in their native cellular context. However, the localization of macromolecules of interest in tomographic volumes can be challenging. Here we present a ligand-inducible labeling strategy for intracellular proteins based on fluorescent, 25-nm-sized, genetically encoded multimeric particles (GEMs). The particles exhibit recognizable structural signatures, enabling their automated detection in cryo-ET data by convolutional neural networks. The coupling of GEMs to green fluorescent protein-tagged macromolecules of interest is triggered by addition of a small-molecule ligand, allowing for time-controlled labeling to minimize disturbance to native protein function. We demonstrate the applicability of GEMs for subcellular-level localization of endogenous and overexpressed proteins across different organelles in human cells using cryo-correlative fluorescence and cryo-ET imaging. We describe means for quantifying labeling specificity and efficiency, and for systematic optimization for rare and abundant protein targets, with emphasis on assessing the potential effects of labeling on protein function. Genetically encoded multimeric particles (GEMs) are 25-nm tags with recognizable structural signatures, which can be used to label specific proteins in mammalian cells to facilitate their subcellular localization in cryo-ET.

Cryo-electron tomography is a powerful label-free tool for visualizing biomolecules in their native cellular context at molecular resolution. However, the precise localisation of biomolecules of interest in the tomographic volumes is challenging. Here, we present a tagging strategy for intracellular protein localisation based on genetically encoded multimeric particles (GEMs). We show the applicability of drug-controlled GEM labelling of endogenous proteins in cryo-electron tomography and cryo-correlative fluorescence imaging in human cells.

Imaging biological macromolecules in their native state with single-particle cryo-electron microscopy (cryo-EM) or in situ cryo-electron tomography (cryo-ET) requires optimized approaches for the preparation and vitrification of biological samples. Here, we describe EasyGrid, a versatile technology enabling systematic, tailored and advanced sample preparation for cellular and structural biology. This automated, standalone platform combines in-line plasma treatment, microfluidic dispensing, blot-less sample spreading, jet-based vitrification and on-the-fly grid quality control using light interferometry to streamline cryo-EM sample optimization. With EasyGrid, we optimized grid preparation for different purified macromolecular complexes and subsequently determined their structure with cryo- EM. We also demonstrated how the platform allows better vitrification of large, mammalian cells compared to standard plunge-freezing. Automated sample preparation with EasyGrid establishes an advanced, high-throughput platform for both single-particle cryo-EM and cellular cryo-ET sample preparation.Competing Interest StatementGergely Papp, Florent Cipriani - pending patent WO 2020/058140 Gergely Papp - European patent application 23 209 700.6

Neurodegenerative diseases pose a significant challenge to modern medicine. Despite significant advances in neurology, current therapeutic approaches often prove insufficient to treat such disorders. This study investigates the neuroprotective effect of extracellular vesicles derived from glial derivates of human-induced pluripotent stem cells. The extracellular vesicle's cargo was characterised by proteomic analysis. The neuroprotective effect was assessed using a model of glutamate excitotoxicity performed on a primary culture of cortical neuroglial cells. The viability of cells was estimated using the MTT test and morphometric analyses. A comprehensive methodology was applied to investigate intracellular mechanisms, integrating assessments of intracellular calcium concentrations, mitochondrial membrane potential, and targeted inhibition of the PI3K-Akt pathway. Transcriptomic analysis of neuroglial cultures was used to validate the role of obtained mechanisms of extracellular vesicle's neuroprotective effect. The obtaining results demonstrated the improvement of neuronal survival by reducing intracellular calcium levels and stabilising mitochondrial membrane potential under glutamate-induced excitotoxicity via PI3K-Akt signalling pathway activation. Moreover, the vesicles contained proteins that contribute to preventing apoptotic processes, activating regeneration of the nervous system, and modulating calcium ion transport and are associated with redox processes. Further transcriptomic analyses of neuroglial cultures treated with EVs showed an up-regulation of genes associated with regeneration, inhibition of calcium ion transport, regulation of membrane depolarisation, and negative regulation of apoptotic pathways.

In the rapidly progressing field of generative models, the development of efficient and high-fidelity text-to-image diffusion systems represents a significant frontier. This study introduces YaART, a novel production-grade text-to-image cascaded diffusion model aligned to human preferences using Reinforcement Learning from Human Feedback (RLHF). During the development of YaART, we especially focus on the choices of the model and training dataset sizes, the aspects that were not systematically investigated for text-to-image cascaded diffusion models before. In particular, we comprehensively analyze how these choices affect both the efficiency of the training process and the quality of the generated images, which are highly important in practice. Furthermore, we demonstrate that models trained on smaller datasets of higher-quality images can successfully compete with those trained on larger datasets, establishing a more efficient scenario of diffusion models training. From the quality perspective, YaART is consistently preferred by users over many existing state-of-the-art models.