Cells are complex mixtures of molecules with different diffusion constants. Like the fragrance particles of a parfum spread in air, the diffusion constant describes how molecules spread from a region of higher concentration to other regions. This is true for particles in the air, as well as for molecules in a cell or tissue. To separate the different diffusive motions of different molecules, scientists need to use specific labeling, hence making it possible to tell apart different molecules by their different label. But sometimes, specific labeling is not possible, like for example when one and the same molecule has different diffusion constants due to, say, differences in shape or orientation. Famous examples are filaments, elongated molecule chains that are common in cells. A filament has different diffusion constants when moving along its axis versus moving perpendicular to it, or rotating. In such cases, it was so far impossible to disentangle different diffusion modes from the motion trajectories of molecules as, for example, extracted from microscopy videos using particle tracking algorithms, without at least some degree of distinct labelling.
Scientists from the group of Ivo Sbalzarini, professor of Scientific Computing for Systems Biology at TU Dresden and group leader at the CSBD and the MPI-CBG, together with colleagues from the Tokyo University of Agriculture and Technology in Japan, have now filled this gap by developing a novel mathematical model of complex mixtures of diffusion modes in particle-tracking data. Their new method was published in the Biophysical Journal.
First author Benjamin Dalton, previously ELBE Postdoctoral Fellow at the CSBD and now postdoc at the FU Berlin, explains, “Our method has unprecedented sensitivity to decide whether a mixture is present, and how many components it contains. But it also turns out to be beneficial for measuring just the diffusion constant of a single molecule. When comparing to the traditional diffusion analysis mostly used nowadays, the MSD analysis, our new method is equally accurate but more robust. This combination of simplicity and sensitivity is just not attainable using MSD analysis.”
The senior author of the work, Itsuo Hanasaki, professor at the Institute of Engineering at the Tokyo University of Agriculture and Technology, stresses the elegant simplicity and beauty of the method, “With this publication, we place our particle-tracking analysis method on a solid mathematical foundation with physical interpretability. Hence, everyone who has to analyze particle-tracking data will benefit from the improved versatility and robustness of this new, very easy to implement and simple method, which does not require any extra hardware in the measurement system.”
Ivo Sbalzarini, who is also affiliated with the TU Dresden Cluster of Excellence „Physics of Life“, concludes, „The beautiful thing about this work is that it is a collaboration between an ELBE postdoc (Benjamin Dalton), a former CSBD Visiting Faculty (Itsuo Hanasaki), and us. The work started in Dresden in 2017, when Prof. Hanasaki was a faculty visitor at the CSBD. That's when the ideas for the project were born in mutual discussions. From this, a year-long collaboration was built, the first results of which are now published. I’m very happy that this became such a nice showcase for how the ELBE Programs of the CSBD contributes to our creativity and worldwide network.“
Original Publication
Benjamin A. Dalton, Ivo F. Sbalzarini, Itsuo Hanasaki: Fundamentals of the logarithmic measure for revealing multimodal diffusion. Biophysical Journal, January 13, 2021
doi: https://doi.org/10.1016/j.bpj.2021.01.001
https://www.cell.com/biophysj/fulltext/S0006-3495(21)00031-X