α-synuclein assemblies sequester neuronal α3-Na+/K+-ATPase and impair Na+ gradient.

Extracellular α-synuclein (α-syn) assemblies can be up-taken by neurons; however, their interaction with the plasma membrane and proteins has not been studied specifically. Here we demonstrate that α-syn assemblies form clusters within the plasma membrane of neurons. Using a proteomic-based approach, we identify the α3-subunit of Na+/K+-ATPase (NKA) as a cell surface partner of α-syn assemblies. The interaction strength depended on the state of α-syn, fibrils being the strongest, oligomers weak, and monomers none. Mutations within the neuron-specific α3-subunit are linked to rapid-onset dystonia Parkinsonism (RDP) and alternating hemiplegia of childhood (AHC). We show that freely diffusing α3-NKA are trapped within α-syn clusters resulting in α3-NKA redistribution and formation of larger nanoclusters. This creates regions within the plasma membrane with reduced local densities of α3-NKA, thereby decreasing the efficiency of Na+ extrusion following stimulus. Thus, interactions of α3-NKA with extracellular α-syn assemblies reduce its pumping activity as its mutations in RDP/AHC.

An aggregation-removal model for the formation and size determination of post-synaptic scaffold domains.

The formation and stability of synapses are key questions in neuroscience. Post-synaptic domains have been classically conceived as resulting from local insertion and turnover of proteins at the synapse. However, insertion is likely to occur outside the post-synaptic domains and advances in single-molecule imaging have shown that proteins diffuse in the plane of the membrane prior to their accumulation at synapses. We quantitatively investigated this scenario using computer simulations and mathematical analysis, taking for definiteness the specific case of inhibitory synapse components, i.e., the glycine receptor (GlyR) and the associated gephyrin scaffolding protein. The observed domain sizes of scaffold clusters can be explained by a dynamic balance between the aggregation of gephyrin proteins diffusing while bound to GlyR and their turnover at the neuron membrane. We also predict the existence of extrasynaptic clusters with a characteristic size distribution that significantly contribute to the size fluctuations of synaptic domains. New super-resolution data for gephyrin proteins established the existence of extrasynaptic clusters the sizes of which are consistent with the model predictions in a range of model parameters. At a general level, our results highlight aggregation with removal as a non-equilibrium phase separation which produces structures of tunable size.

Computer-aided diagnosis tool for cervical cancer screening with weakly supervised localization and detection of abnormalities using adaptable and explainable classifier.

While pap test is the most common diagnosis methods for cervical cancer, their results are highly dependent on the ability of the cytotechnicians to detect abnormal cells on the smears using brightfield microscopy. In this paper, we propose an explainable region classifier in whole slide images that could be used by cyto-pathologists to handle efficiently these big images (100,000x100,000 pixels). We create a dataset that simulates pap smears regions and uses a loss, we call classification under regression constraint, to train an efficient region classifier (about 66.8% accuracy on severity classification, 95.2% accuracy on normal/abnormal classification and 0.870 KAPPA score). We explain how we benefit from this loss to obtain a model focused on sensitivity and, then, we show that it can be used to perform weakly supervised localization (accuracy of 80.4%) of the cell that is mostly responsible for the malignancy of regions of whole slide images. We extend our method to perform a more general detection of abnormal cells (66.1% accuracy) and ensure that at least one abnormal cell will be detected if malignancy is present. Finally, we experiment our solution on a small real clinical slide dataset, highlighting the relevance of our proposed solution, adapting it to be as easily integrated in a pathology laboratory workflow as possible, and extending it to make a slide-level prediction.

Data in support of the identification of neuronal and astrocyte proteins interacting with extracellularly applied oligomeric and fibrillar α-synuclein assemblies by mass spectrometry.

α-Synuclein (α-syn) is the principal component of Lewy bodies, the pathophysiological hallmark of individuals affected by Parkinson disease (PD). This neuropathologic form of α-syn contributes to PD progression and propagation of α-syn assemblies between neurons. The data we present here support the proteomic analysis used to identify neuronal proteins that specifically interact with extracellularly applied oligomeric or fibrillar α-syn assemblies (conditions 1 and 2, respectively) (doi: 10.15252/embj.201591397[1]). α-syn assemblies and their cellular partner proteins were pulled down from neuronal cell lysed shortly after exposure to exogenous α-syn assemblies and the associated proteins were identified by mass spectrometry using a shotgun proteomic-based approach. We also performed experiments on pure cultures of astrocytes to identify astrocyte-specific proteins interacting with oligomeric or fibrillar α-syn (conditions 3 and 4, respectively). For each condition, proteins interacting selectively with α-syn assemblies were identified by comparison to proteins pulled-down from untreated cells used as controls. The mass spectrometry data, the database search and the peak lists have been deposited to the ProteomeXchange Consortium database via the PRIDE partner repository with the dataset identifiers PRIDE: PXD002256 to PRIDE: PXD002263 and doi: 10.6019/PXD002256 to 10.6019/PXD002263.