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  • Featured in Physics
  • Open Access

Intrinsic Cell-to-Cell Variance from Experimental Single-Cell Motility Data

Anton Klimek1, Johannes C. J. Heyn2, Debasmita Mondal3,4, Sophia Schwartz5, Joachim O. Rädler2, Prerna Sharma3,6, Stephan Block5, and Roland R. Netz1,*

  • *Contact author: rnetz@physik.fu-berlin.de
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PRX Life 3, 023015 – Published 17 June, 2025

DOI: https://doi.org/10.1103/77t1-2qvv

Abstract

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 actual differences between individual objects, which is the quantity of ultimate interest. We develop the theoretical framework to separate these three effects based on the generalized Langevin equation, which constitutes the most general description of active and passive dynamics, as it derives from the underlying general many-body Hamiltonian for the studied system without approximations. We apply our methodology to determine intrinsic cell-to-cell differences of living and actively moving 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 spread 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 it opens up new possibilities to estimate population heterogeneity on the single-organism level in a nondestructive manner. Our framework is not limited to motility properties but can be readily applied to other experimental time-series data.

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synopsis

Cells Show “Personality” When They Move

Published 17 June, 2025

A study of cellular motion demonstrates a method for isolating individual cellular behavior from environmentally induced random motion.

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PRX Life 3, 023015– Published 17 June, 2025
  • Received 18 October 2024
  • Accepted 9 May 2025
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Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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