DEVELOPMENT OF A MICROFLUIDIC DEVICE FOR THE STUDY OF NEUROLOGICAL DISORDER-RELATED PROTEINS PHASE SEPARATION

Liquid-liquid phase separation (LLPS) corresponds to the thermodynamic phenomenon in which a component of a previously homogeneous solution undergoes phase transition due to some factors, separating in two or more distinct phases that coexist simultaneously in a system, as observed in oil-in-water emulsion. Recently, the study of LLPS in a biological context has emerged, revolutionizing this research field, since it has been hypothesized to play a crucial and vital role in cell organization, origins of life, and human health. Considering the state-of-the-art, current methods for analyzing the condensates formed through LLPS use fluorescence-based techniques in multiple well microplate-using and microfluidic device-based approaches, resulting in phase diagrams. However, several issues faced can be highlighted, such as: dependency on tagging the protein of interest with fluorescent markers, not mimicking with high fidelity the real system and being associated with many confusing factors; high sample consumption; being costly; and having as outcome a measurement that doesn’t allow a complex interpretation of the mechanism that is undergoing, lacking information about how those condensates are formed, the structure interactions and inside organization. From this perspective, aiming to contribute to this cutting-edge and breakthrough scientific research area, and attempting to solve or, at least, minimize most of the issues faced, it is implemented innovative and interdisciplinary approaches. This study proposes the development of a microfluidic device, embracing an interdigitated gold electrode, to improve and enhance the detection and the analysis of protein phase separation, particularly those related to neurodegenerative and neurodevelopmental pathologies, and neurological disturbs, which affect approximately one in three people during lifetime. Contrasting current techniques, the proposed approach aims to make it possible to analyze over time and perform electrochemical measurements, dispensing the label dependency, and turning possible signatures identification by implementing neural networks and machine learning for data analysis. Finally, owing to the proteins analyzed, this work aspires to question and contribute to gender epistemology and the inclusion of disorders and social groups frequently neglected in science.

This work is an undergraduate thesis supported by Ilum School of Science and CNPEM. G.G.D. and V.Y.U.N. contributed equally to this work.