T cell morphodynamics reveal periodic shape oscillations in three-dimensional migration

Abstract

T cells use sophisticated shape dynamics (morphodynamics) to migrate towards and neutralize infected and cancerous cells. However, there is limited quantitative understanding of the migration process in three-dimensional extracellular matrices (ECMs) and across timescales. Here, we leveraged recent advances in lattice light-sheet microscopy to quantitatively explore the three-dimensional morphodynamics of migrating T cells at high spatio-temporal resolution. We first developed a new shape descriptor based on spherical harmonics, incorporating key polarization information of the uropod. We found that the shape space of T cells is low-dimensional. At the behavioural level, run-and-stop migration modes emerge at approximately 150 s, and we mapped the morphodynamic composition of each mode using multiscale wavelet analysis, finding ‘stereotyped’ motifs. Focusing on the run mode, we found morphodynamics oscillating periodically (every approx. 100 s) that can be broken down into a biphasic process: front-widening with retraction of the uropod, followed by a rearward surface motion and forward extension, where intercalation with the ECM in both of these steps likely facilitates forward motion. Further application of these methods may enable the comparison of T cell migration across different conditions (e.g. differentiation, activation, tissues and drug treatments) and improve the precision of immunotherapeutic development.

Heterogeneous anomalous transport in cellular and molecular biology

“…The hemocytes are switching between different levels of persistent motility, rather than distinct subpopulations of cells having altered dynamics. Interestingly, local (time resolved) anomalous exponents (and also local generalized diffusion coefficients) of individual hemocytes were found to exhibit oscillatory behaviour which could be a manifestation of a positive feedback loop between actin flows and maintenance of cell polarity [299,300] and/or similar periodic shape oscillations to those observed during the run-and-stop motions of T cells [301]. Upon their contact, we found that the hemocyte motion was synchronised, although their clear sense of directional motion was lost [74].…”

“…The heterogeneity of motility emerged from a positive feedback loop between actin flows and the maintenance of cell polarity in motile cells which results in a higher persistence of motility for faster cells [299,300]. Run-and-stop motion in the three-dimensional migration of T cells with periodic shape oscillations was observed [301].…”

Learning dynamical models of single and collective cell migration: a review

“…(G) Image fluctuation analysis based on high-resolution live imaging of polarity factors allows causality inference of actin binding factors and how they control protrusion growth [232][233][234]. (H) Segmentation of the 2D or 3D shape of cells can be integrated in shape space models of morphodynamics, providing a dimensional reduction of complex dynamics [235][236][237][238]. (I) Integrating traction force microscopy datasets has been done in machine learning models predicting forces based on protein concentrations [239].…”

“…Thus, a first challenge is to determine the dominant contributions to the cell morphology through dimension reduction. By identifying the principle components of the cell shape, recent works have proposed to study cell morphology in a low dimensional space feature space [235][236][237][238] (figure 8(H)). From an analysis point of view, these approaches have been very successful by demonstrating that clustering in shape space can be predictive of metastatic potential [242,243], stem cell lineage decisions [244] and drug response [235], highlighting the rich information content of cell morphologies.…”

“…From an analysis point of view, these approaches have been very successful by demonstrating that clustering in shape space can be predictive of metastatic potential [242,243], stem cell lineage decisions [244] and drug response [235], highlighting the rich information content of cell morphologies. Furthermore, morphodynamic feature approaches have allowed comparative mapping of different cell types [245], identification of migration strategies in 3D matrices [238,246], and revealed adaptive switching between different modes of mesenchymal migration [247].…”