Intrinsic cell-to-cell variance from experimental single-cell motility data

• Main Authors: Klimek, Anton, Heyn, Johannes C. J, Mondal, Debasmita, Schwartz, Sophia, Rädler, Joachim O, Sharma, Prerna, Block, Stephan, Netz, Roland R
• Format: Journal Article
• Language: English
• Published: 18.10.2024
• Subjects: Physics - Biological Physics
• DOI: 10.48550/arxiv.2410.14561

When analyzing the individual positional dynamics of an ensemble of moving objects, the extracted parameters that characterize the motion of individual objects, such as the mean-squared instantaneous velocity or the diffusivity, exhibit a spread that is due to the convolution of three different effects: i) Motion stochasticity, caused by the fluctuating environment and enhanced by limited observation time, ii) measurement errors that depend on details of the detection technique, and iii) the intrinsic parameter variance that characterizes differences between individual objects, the quantity of ultimate interest. We develop the theoretical framework to separate these effects using the generalized Langevin equation (GLE), which constitutes the most general description of active and passive dynamics, as it derives from the general underlying many-body Hamiltonian for the studied system without approximations. We apply our methodology to determine intrinsic cell-to-cell differences of living human breast-cancer cells, algae cells and, as a benchmark, size differences of passively moving polystyrene beads in water. We find algae and human breast-cancer cells to exhibit significant individual differences, reflected by the spreading of the intrinsic mean-squared instantaneous velocity over two orders of magnitude, which is remarkable in light of the genetic homogeneity of the investigated breast-cancer cells and highlights their phenotypical diversity. Quantification of the intrinsic variance of single-cell properties is relevant for infection biology, ecology and medicine and opens up new possibilities to estimate population heterogeneity on the single-organism level in a non-destructive manner. Our framework is not limited to motility properties but can be readily applied to general experimental time-series data.