Discrimination between Alpha-Synuclein Protein Variants with a Single Nanometer-Scale Pore

Unraveling RNA Conformation Dynamics in Mitochondrial Encephalomyopathy, Lactic Acidosis, and Stroke-like Episode Syndrome with Solid-State Nanopores

This study investigates transfer ribonucleic acid (tRNA) conformational dynamics in the context of MELAS (mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes) using solid-state silicon nitride (SiN) nanopore technology. SiN nanopores in thin membranes with specific dimensions exhibit high signal resolution, enabling real-time and single-molecule electronic detection of tRNA conformational changes. We focus on human mitochondrial tRNALeu(UAA) (mt-Leu(UAA)) that decodes Leu codons UUA/UUG (UUR) during protein synthesis on the mt-ribosome. The single A14G substitution in mt-Leu(UAA) is the major cause of MELAS disease. Measurements of current blockades and dwell times reveal distinct conformational dynamics of the wild-type (WT) and the A14G variant of mt-Leu(UAA) in response to the conserved post-transcriptional m1G9 methylation. While the m1G9-modified WT transcript adopts a more stable structure relative to the unmodified transcript, the m1G9-modified MELAS transcript adopts a less stable structure relative to the unmodified transcript. Notably, these differential features were observed at 0.4 M KCl, but not at 3 M KCl, highlighting the importance of experimental settings that are closer to physiological conditions. This work demonstrates the feasibility of the nanopore platform to discern tRNA molecules that differ by a single-nucleotide substitution or by a single methylation event, providing an important step forward to explore changes in the conformational dynamics of other RNA molecules in human diseases.

Characterizing Prion-Like Protein Aggregation: Emerging Nanopore-Based Approaches

Prion-like protein aggregation is characteristic of numerous neurodegenerative diseases, such as Alzheimer's and Parkinson's diseases. This process involves the formation of aggregates ranging from small and potentially neurotoxic oligomers to highly structured self-propagating amyloid fibrils. Various approaches are used to study protein aggregation, but they do not always provide continuous information on the polymorphic, transient, and heterogeneous species formed. This review provides an updated state-of-the-art approach to the detection and characterization of a wide range of protein aggregates using nanopore technology. For each type of nanopore, biological, solid-state polymer, and nanopipette, discuss the main achievements for the detection of protein aggregates as well as the significant contributions to the understanding of protein aggregation and diagnostics.

Influence of Seed structure on Volume distribution of α-Synuclein Oligomer at Early Stages of Aggregation using nanopipette

Understanding α-synuclein aggregation is crucial in the context of Parkinson's disease. The objective of this study was to investigate the influence of aggregation induced by preformed seeding on the volume of oligomers during the early stages, using a label-free, single-molecule characterization approach. By utilizing nanopipettes of varying sizes, the volume of the oligomers can be calculated from the amplitude of the current blockade and pipette geometry. Further investigation of the aggregates formed over time in the presence of added seeds revealed an acceleration in the formation of large aggregates and the existence of multiple distinct populations of oligomers. Additionally, we observed that spontaneously formed seeds inhibited the formation of smaller oligomers, in contrast to the effect of HNE seeds. These results suggest that the seeds play a crucial role in the formation of oligomers and their sizes during the early stages of aggregation, whereas the classical thioflavin T assay remains negative.

Identification of Conformational Variants for Bradykinin Biomarker Peptides from a Biofluid Using a Nanopore and Machine Learning

There is a current need to develop methods for the sensitive detection of peptide biomarkers in complex mixtures of molecules, such as biofluids, to enable early disease detection. Moreover, to our knowledge, there is currently no detection method capable of identifying the different conformations of a peptide biomarker differing by a single amino acid. Single-molecule nanopore sensing promises to provide this level of resolution. In order to be able to identify these differences in a biofluid such as serum, it is necessary to carefully characterize electrical parameters to obtain specific signatures of each biomarker population observed. We are interested here in a family of peptide biomarkers, kinins such as bradykinin and des-Arg9 bradykinin, that are involved in many disabling pathologies (allergy, asthma, angioedema, sepsis, or cancer). We show the proof of concept for direct identification of these biomarkers in serum at the single-molecule level using a protein nanopore. Each peptide exhibits two unique electrical signatures attributed to specific conformations in bulk. The same signatures are found in serum, allowing their discrimination and identification in a complex mixture such as biofluid. To extend the utility of our experimental results, we developed a principal component analysis approach to define the most relevant electrical parameters for their identification. Finally, we used semisupervised classification to assign each event type to a specific biomarker at physiological serum concentration. In the future, single-molecule scale analysis of peptide biomarkers using a powerful nanopore coupled with machine learning will facilitate the identification and quantification of other clinically relevant biomarkers from biofluids.

Multiplexed Digital Characterization of Misfolded Protein Oligomers via Solid-State Nanopores

Misfolded protein oligomers are of central importance in both the diagnosis and treatment of Alzheimer's and Parkinson's diseases. However, accurate high-throughput methods to detect and quantify oligomer populations are still needed. We present here a single-molecule approach for the detection and quantification of oligomeric species. The approach is based on the use of solid-state nanopores and multiplexed DNA barcoding to identify and characterize oligomers from multiple samples. We study α-synuclein oligomers in the presence of several small-molecule inhibitors of α-synuclein aggregation as an illustration of the potential applicability of this method to the development of diagnostic and therapeutic methods for Parkinson's disease.