GPR37 processing in neurodegeneration: a potential marker for Parkinson's Disease progression rate.

The orphan G protein-coupled receptor 37 (GPR37), widely associated with Parkinson's disease (PD), undergoes proteolytic processing under physiological conditions. The N-terminus domain is proteolyzed by a disintegrin and metalloproteinase 10 (ADAM-10), which generates various membrane receptor forms and ectodomain shedding (ecto-GPR37) in the extracellular environment. We investigated the processing and density of GPR37 in several neurodegenerative conditions, including Lewy body disease (LBD), multiple system atrophy (MSA), corticobasal degeneration (CBD), progressive supranuclear palsy (PSP), and Alzheimer's disease (AD). The presence of ecto-GPR37 peptides in the cerebrospinal fluid (CSF) of PD, MSA, CBD and PSP patients was assessed through an in-house nanoluciferase-based immunoassay. This study identified increased receptor processing in early-stage LBD within the PFC and striatum, key brain areas in neurodegeneration. In MSA only the 52 kDa form of GPR37 appeared in the striatum. This form was also significantly elevated in the striatum of AD necropsies. On the contrary, GPR37 processing remained unchanged in the brains of CBD and PSP patients. Furthermore, while CSF ecto-GPR37 increased in PD patients, its levels remained unchanged in MSA, CBD, and PSP subjects. Importantly, patients with PD with rapid progression of the disease did not have elevated ecto-GPR37 in the CSF, while those with slow progression showed a significant increase, suggesting a possible prognostic use of ecto-GPR37 in PD. This research underscores the distinctive processing and density patterns of GPR37 in neurodegenerative diseases, providing crucial insights into its potential role as an indicator of PD progression rates.

Interneuron diversity in the human dorsal striatum.

Deciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangling the complex neurological and psychiatric diseases affecting this brain structure. We performed snRNA-seq and spatial transcriptomics of postmortem human caudate nucleus and putamen samples to elucidate the diversity and abundance of interneuron populations and their inherent transcriptional structure in the human dorsal striatum. We propose a comprehensive taxonomy of striatal interneurons with eight main classes and fourteen subclasses, providing their full transcriptomic identity and spatial expression profile as well as additional quantitative FISH validation for specific populations. We have also delineated the correspondence of our taxonomy with previous standardized classifications and shown the main transcriptomic and class abundance differences between caudate nucleus and putamen. Notably, based on key functional genes such as ion channels and synaptic receptors, we found matching known mouse interneuron populations for the most abundant populations, the recently described PTHLH and TAC3 interneurons. Finally, we were able to integrate other published datasets with ours, supporting the generalizability of this harmonized taxonomy.

Demystifying dimensionality reduction techniques in the 'omics' era: A practical approach for biological science students.

Dimensionality reduction techniques are essential in analyzing large 'omics' datasets in biochemistry and molecular biology. Principal component analysis, t-distributed stochastic neighbor embedding, and uniform manifold approximation and projection are commonly used for data visualization. However, these methods can be challenging for students without a strong mathematical background. In this study, intuitive examples were created using COVID-19 data to help students understand the core concepts behind these techniques. In a 4-h practical session, we used these examples to demonstrate dimensionality reduction techniques to 15 postgraduate students from biomedical backgrounds. Using Python and Jupyter notebooks, our goal was to demystify these methods, typically treated as "black boxes", and empower students to generate and interpret their own results. To assess the impact of our approach, we conducted an anonymous survey. The majority of the students agreed that using computers enriched their learning experience (67%) and that Jupyter notebooks were a valuable part of the class (66%). Additionally, 60% of the students reported increased interest in Python, and 40% gained both interest and a better understanding of dimensionality reduction methods. Despite the short duration of the course, 40% of the students reported acquiring research skills necessary in the field. While further analysis of the learning impacts of this approach is needed, we believe that sharing the examples we generated can provide valuable resources for others to use in interactive teaching environments. These examples highlight advantages and limitations of the major dimensionality reduction methods used in modern bioinformatics analysis in an easy-to-understand way.

Integrated analysis of transcriptomic data reveals the platelet response in COVID-19 disease.

COVID-19 is associated with an increased risk of thrombotic events. However, the pathogenesis of these complications is unclear and reports on platelet infection and activation by the virus are conflicting. Here, we integrated single-cell transcriptomic data to elucidate whether platelet activation is a specific response to SARS-CoV-2 infection or a consequence of a generalized inflammatory state. Although platelets from patients infected with SARS-CoV-2 over expressed genes involved in activation and aggregation when compared to healthy controls; those differences disappeared when the comparison was made with patients with generalized inflammatory conditions of other etiology than COVID-19. The membrane receptor for the virus, ACE-2, was not expressed by infected or control platelets. Our results suggest that platelet activation in patients with severe COVID-19 is mainly a consequence of a systemic inflammatory state than direct invasion and activation.

Inkjet-printed PEDOT:PSS multi-electrode arrays for low-cost in vitro electrophysiology.

Multi-electrode arrays (MEAs) have become a key element in the study of cellular phenomena in vitro. Common modern MEAs are still based on costly microfabrication techniques, making them expensive tools that researchers are pushed to reuse, compromising the reproducibility and the quality of the acquired data. There is a need to develop novel fabrication strategies, able to produce disposable devices that incorporate advanced technologies beyond the standard metal electrodes on rigid substrates. Here we present an innovative fabrication process for the production of polymer-based flexible MEAs. The device fabrication exploited inkjet printing, as this low-cost manufacturing method allows for an easy and reliable patterning of conducting polymers. Poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS) was used as the sole conductive element of the MEAs. The physical structure and the electrical properties of the plastic/printed MEAs (pMEAs) were characterised, showing a low impedance that is maintained also in the long term. The biocompatibility of the devices was demonstrated, and their capability to successfully establish a tight coupling with cells was proved. Furthermore, the pMEAs were used to monitor the extracellular potentials from cardiac cell cultures and to record high quality electrophysiological signals from them. Our results validate the use of pMEAs as in vitro electrophysiology platforms, pushing for the adoption of innovative fabrication techniques and the use of new materials for the production of MEAs.

Cost-effective and multifunctional acquisition system for in vitro electrophysiological investigations with multi-electrode arrays.

In vitro multi-electrode array (MEA) technology is nowadays involved in a wide range of applications beyond neuroscience, such as cardiac electrophysiology and bio-interface studies. However, the cost of commercially available acquisition systems severely limits its adoption outside specialized laboratories with high budget capabilities. Thus, the availability of low-cost methods to acquire signals from MEAs is important to allow research labs worldwide to exploit this technology for an ever-expanding pool of experiments independently from their economic possibilities. Here, we provide a comprehensive toolset to assemble a multifunctional in vitro MEA acquisition system with a total cost 80% lower than standard commercial solutions. We demonstrate the capabilities of this acquisition system by employing it to i) characterize commercial MEA devices by means of electrical impedance measurements ii) record activity from cultures of HL-1 cells extracellularly, and iii) electroporate HL-1 cells through nanostructured MEAs and record intracellular signals.

Membrane Poration Mechanisms at the Cell-Nanostructure Interface.

3D vertical nanostructures have become one of the most significant methods for interfacing cells and the nanoscale and for accessing significant intracellular functionalities such as membrane potential. As this intracellular access can be induced by means of diverse cellular membrane poration mechanisms, it is important to investigate in detail the cell condition after membrane rupture for assessing the real effects of the poration techniques on the biological environment. Indeed, differences of the membrane dynamics and reshaping have not been observed yet when the membrane-nanostructure system is locally perturbed by, for instance, diverse membrane breakage events. In this work, new insights are provided into the membrane dynamics in case of two different poration approaches, optoacoustic- and electro-poration, both mediated by the same 3D nanostructures. The experimental results offer a detailed overview on the different poration processes in terms of electrical recordings and membrane conformation.

Structure-based classification of FAD binding sites: A comparative study of structural alignment tools.

A total of six different structural alignment tools (TM-Align, TriangleMatch, CLICK, ProBis, SiteEngine and GA-SI) were assessed for their ability to perform two particular tasks: (i) discriminating FAD (flavin adenine dinucleotide) from non-FAD binding sites, and (ii) performing an all-to-all comparison on a set of 883 FAD binding sites for the purpose of classifying them. For the first task, the consistency of each alignment method was evaluated, showing that every method is able to distinguish FAD and non-FAD binding sites with a high Matthews correlation coefficient. Additionally, GA-SI was found to provide alignments different from those of the other approaches. The results obtained for the second task revealed more significant differences among alignment methods, as reflected in the poor correlation of their results and highlighted clearly by the independent evaluation of the structural superimpositions generated by each method. The classification itself was performed using the combined results of all methods, using the best result found for each comparison of binding sites. A number of different clustering methods (Single-linkage, UPGMA, Complete-linkage, SPICKER and k-Means clustering) were also used. The groups of similar binding sites (proteins) or clusters generated by the best performing method were further analyzed in terms of local sequence identity, local structural similarity and conservation of analogous contacts with the FAD ligands. Each of the clusters was characterized by a unique set of structural features or patterns, demonstrating that the groups generated truly reflect the structural diversity of FAD binding sites. Proteins 2016; 84:1728-1747. © 2016 Wiley Periodicals, Inc.

Comparison of non-sequential sets of protein residues.

A methodology for performing sequence-free comparison of functional sites in protein structures is introduced. The method is based on a new notion of similarity among superimposed groups of amino acid residues that evaluates both geometry and physico-chemical properties. The method is specifically designed to handle disconnected and sparsely distributed sets of residues. A genetic algorithm is employed to find the superimposition of protein segments that maximizes their similarity. The method was evaluated by performing an all-to-all comparison on two separate sets of ligand-binding sites, comprising 47 protein-FAD (Flavin-Adenine Dinucleotide) and 64 protein-NAD (Nicotinamide-Adenine Dinucleotide) complexes, and comparing the results with those of an existing sequence-based structural alignment tool (TM-Align). The quality of the two methodologies is judged by the methods' capacity to, among other, correctly predict the similarities in the protein-ligand contact patterns of each pair of binding sites. The results show that using a sequence-free method significantly improves over the sequence-based one, resulting in 23 significant binding-site homologies being detected by the new method but ignored by the sequence-based one.