Machine learning opens a doorway for microrheology with optical tweezers in living systems

It has been argued [Tassieri, $\mathit{\text{Soft}}\mathit{\text{Matter}}$, 2015, $\mathbf{\text{11}}$, 5792] that linear microrheology with optical tweezers (MOT) of living systems ``$\mathit{\text{is}}\mathit{\text{not}}\mathit{\text{an}}\mathit{\text{option}}$‘’, because of the wide gap between the observation time required to collect statistically valid data and the mutational times of the organisms under study. Here, we have taken a first step towards a possible solution of this problem by exploiting modern machine learning (ML) methods to reduce the duration of MOT measurements from several tens of minutes down to one second. This has been achieved by focusing on the analysis of computer simulated trajectories of an optically trapped particle suspended in a set of Newtonian fluids having viscosity values spanning three orders of magnitude, i.e. from ${10}^{-3}$ to $1$ Pa$\cdot$s. When the particle trajectory is analysed by means of conventional statistical mechanics principles, we explicate for the first time in literature the relationship between the required duration of MOT experiments ($T_m$) and the fluids relative viscosity (${\eta }_r$) to achieve an uncertainty as low as $1\mathrm{\%}$; i.e., $T_m\cong 17{\eta }_r^3$ minutes. This has led to further evidences explaining why conventional MOT measurements commonly underestimate the materials’ viscoelastic properties, especially in the case of high viscous fluids or soft-solids such as gels and cells. Finally, we have developed a ML algorithm to determine the viscosity of Newtonian fluids that uses feature extraction on raw trajectories acquired at a kHz and for a duration of only one second, yet capable of returning viscosity values carrying an error as low as $\sim 0.3\mathrm{\%}$ at best; hence the opening of a doorway for MOT in living systems.