Profiling Dynamic Patterns of Single-cell Motility

Abstract

Cell motility is essential to many biological processes as cells navigate and interact within their local microenvironments. Currently, most methods to quantify cell motility rely on the ability to follow and track individual cells. However, results from these approaches are typically reported as averaged values across cell populations. While these approaches offer biological simplicity, it limits the ability to assess cellular heterogeneity and infer generalizable patterns of single-cell behaviors at baseline or after perturbations. Here, we present CaMI, a computational framework that takes advantage of the single-cell nature of cell motility data to identify and classify distinct spatio-temporal behaviors of single cells. Using CaMI, we demonstrate the ability to robustly classify single-cell motility patterns in a large dataset (n=74,253 cells), quantify spatio-temporal heterogeneities, determine motility patterns in unclassified cells, and provide a visualization scheme for direct interpretation of dynamic cell behaviors. Furthermore, as a biological proof of concept, we investigate the biphasic spatial and temporal responses of T-cell lymphoma cells moving in Collagen-I gels of varying collagen concentrations and predict the dimensionality (2D vs. 3D) of matched cellular conditions based solely on spatio-temporal heterogeneities. Together, we present a multivariate framework to robustly classify emergent patterns of single-cell behaviors, highlighting cellular heterogeneity as a critical feature that establishes the behaviors of cellular populations.

Geometric changes in the nucleoids of Deinococcus radiodurans reveal involvement of new proteins in recovery from ionizing radiation

“…The optimal number of clusters were determined using the ‘elbow method’, or the beginning of the elbow of the inertia/silhouette graph shown in Supplementary Figure S19. Cluster partitioning was determined by the k-means algorithm, an unsupervised approach that only requires the number of clusters to be input by the user [62, 63]. Approximately 470,000 cells encompassing all experimental conditions were used to capture the entirety of possible morphological phenotypes.…”

cellPLATO – an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data

“…Advances in fast, gentle, large field-of-view microscopy, combined with new methods for image segmentation ( Lee et al, 2022 ; Pachitariu and Stringer, 2022 ; Stirling et al, 2021 ; Stringer et al, 2021 ; Tsai et al, 2019 ; Weigert et al, 2020 ) and cell tracking ( Bove et al, 2017 ; Cuny et al, 2022 ; Ershov et al, 2022 ; Ulicna et al, 2021 ), have enabled the analysis of thousands of cells over time and brought microscopy imaging into the realm of big data. Existing tools highlight the importance of morpho-kinetic analysis and phenomics, allowing separation of cell behaviours, but often require intermediate coding knowledge to use and generate latent space behaviour categories without de-abstractification ( Crainiciuc et al, 2022 ; Freckmann et al, 2022 ; Kimmel et al, 2021 , 2018 ; Liu et al, 2023 ; Maity et al, 2022 preprint; Molina-Moreno et al, 2022 ; Wiggins et al, 2023 ). We sought to develop a tool to measure and classify cell populations downstream of such methods.…”

cellPLATO: an unsupervised method for identifying cell behaviour in heterogeneous cell trajectory data

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