Fluctuations in protein aggregation: Design of preclinical screening for early diagnosis of neurodegenerative disease

TL;DR

This paper investigates how intrinsic fluctuations influence protein aggregation in small volumes, proposing a computational approach to improve early diagnosis of neurodegenerative diseases through microfluidic analysis.

Contribution

It introduces a theoretical framework for analyzing protein aggregation fluctuations in micron-scale samples and develops a strategy to quantify detection errors for diagnostic purposes.

Findings

Intrinsic fluctuations govern protein aggregation onset in small volumes

Theoretical distribution of fluctuations is estimated

A strategy for quantifying detection errors is proposed

Abstract

Autocatalytic fibril nucleation has recently been proposed to be a determining factor for the spread of neurodegenerative diseases, but the same process could also be exploited to amplify minute quantities of protein aggregates in a diagnostic context. Recent advances in microfluidic technology allow analysis of protein aggregation in micron-scale samples potentially enabling such diagnostic approaches, but the theoretical foundations for the analysis and interpretation of such data are so far lacking. Here we study computationally the onset of protein aggregation in small volumes and show that the process is ruled by intrinsic fluctuations whose volume dependent distribution we also estimate theoretically. Based on these results, we develop a strategy to quantify in silico the statistical errors associated with the detection of aggregate containing samples. Our work opens a new perspective on the forecasting of protein aggregation in asymptomatic subjects.