CellTracksColab — A platform for compiling, analyzing, and exploring tracking data

Gómez-de-Mariscal, Estibaliz; Grobe, Hanna; Pylvänäinen, Joanna W.; Xénard, Laura; Henriques, Ricardo; Tinevez, Jean-Yves; Jacquemet, Guillaume

doi:10.1101/2023.10.20.563252

Cited by 5 publications

(2 citation statements)

References 45 publications

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“…It should also be noted that although cellPLATO is built to be simple to use for those new to coding, users are currently required to install the software locally. The first part of the cellPLATO workflow (feature extraction, UMAP, HDBSCAN, exemplar behaviour extraction at single timepoints and fingerprinting between conditions) has been reproduced in Google Colab ( Jacquemet, 2023 preprint) providing browser-based access to remote computing resources, as well as using Docker – a containerized approach to provide a smooth local install process ( Hidalgo-Cenalmor et al, 2024 ). Integration of cellPLATO with these platforms might be a future direction for the software.…”

Section: Discussionmentioning

confidence: 99%

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

Shannon,

Eisman,

Lowe

et al. 2024

Journal of Cell Science

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Advances in imaging, segmentation, and tracking have led to the routine generation of large and complex microscopy datasets. New tools are required to process this ‘phenomics’ type data. Cell PLasticity Analysis TOol (cellPLATO) is a Python-based analysis software designed for measurement and classification of cell behaviours based on clustering features of cell morphology and motility. Used after segmentation and tracking, the tool extracts features from each cell per timepoint, using them to segregate cells into dimensionally reduced behavioural subtypes. Resultant cell tracks describe a ‘behavioural ID’ at each timepoint and similarity analysis allows the grouping of behavioural sequences into discrete trajectories with assigned IDs. Here, we use cellPLATO to investigate the role of IL-15 in modulating human NK cell migration on ICAM-1 or VCAM-1. We find 8 behavioural subsets of NK cells based on their shape and migration dynamics between single timepoints, and 4 trajectories based on sequences of these behaviours over time. Therefore, using cellPLATO we show that IL-15 increases plasticity between cell migration behaviours and that different integrin ligands induce different forms of NK cell migration.

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Section: Discussionmentioning

confidence: 99%

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

Shannon,

Eisman,

Lowe

et al. 2024

Journal of Cell Science

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“…Where necessary, time lapse videos were stabilised against drift using custom code as in (Oikonomou et al, 2023), inspired by Fast4Dreg (Pylvänäinen et al, 2023). Cell morphometry features generated through this pipeline were aggregated across biological replicates and plotted in UMAP space, generally following the approach of cellPLATO (Shannon et al, 2023) and adapting code from ColabTracks (Jacquemet, 2024). Specifically, UMAP embedding was based on the following features: magnitude of applied strain, deformed aspect ratio, un-deformed aspect ratio, current cell area, undeformed cell area, orientation of the cell major axis relative to the medio-lateral embryonic axis, minor axis length, major axis length, cell perimeter, and shape solidity (area / convex area)].…”

Section: Methodsmentioning

confidence: 99%

Application of tissue-scale tension to avian epitheliain vivoto study multiscale mechanics and inter-germ layer coupling

Oikonomou,

Calvary,

Cirne

et al. 2024

Preprint

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As cross-disciplinary approaches drawing from physics and mechanics have increasingly influenced our understanding of morphogenesis, the tools available to measure and perturb physical aspects of embryonic development have expanded as well. However, it remains a challenge to measure mechanical properties and apply exogenous tissue-scale forcesin vivo, particularly for epithelia. Exploiting the size and accessibility of the developing chick embryo, here we describe a simple technique to quantitatively apply exogenous forces on the order of ∼1-100µN to the endodermal epithelium. To demonstrate the utility of this approach, we performed a series of proof-of-concept experiments that reveal fundamental and unexpected mechanical behaviors in the early chick embryo, including mechanotype heterogeneity among cells of the midgut endoderm, complex non-cell autonomous effects of actin disruption, and a high degree of mechanical coupling between the endoderm and adjacent paraxial mesoderm. To illustrate the broader utility of this method, we determined that forces on the order of ∼ 10µN are sufficient to unzip the neural tube during primary neurulation. Together, these findings provide basic insights into the mechanics of embryonic epitheliain vivoin the early avian embryo, and provide a useful tool for future investigations of how morphogenesis is influenced by mechanical factors.Graphical AbstractSummary StatementA simple approach is devised for quantitatively applying tension to epitheliain vivo, and used to study the mechanics of embryonic epithelia in the chick embryo.

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BEHAV3D Tumor Profiler to map heterogeneous cancer cell behavior in the tumor microenvironment

Rios-Jimenez,

Zomer,

Collot

et al. 2024

Preprint

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Intravital microscopy (IVM) enables live imaging of animals at single-cell level, offering essential insights into cancer progression. This technique allows for the observation of single-cell behaviors within their natural 3D tissue environments, shedding light on how genetic and microenvironmental changes influence the complex dynamics of tumors. The complexity of data generated by IVM often surpasses the capabilities of conventional analyses accessible to biomedical scientists, thereby neglecting single-cell heterogeneity and limiting the exploration of microenvironmental influences on cellular behavior without bias. To address this challenge, here we introduce BEHAV3D Tumor Profiler (BEHAV3D-TP), a user-friendly computational framework designed for the comprehensive analysis of single tumor cell behaviors and their interactions with the tumor microenvironment (TME). BEHAV3D-TP facilitates unbiased profiling of cancer cell dynamics without requiring advanced computational expertise. Here, we apply BEHAV3D-TP to study diffuse midline glioma (DMG), a highly aggressive pediatric brain tumor characterized by invasive growth. Our analysis reveals that distinct migratory behaviors of DMG cells correlate with specific TME components such as tumor-associated macrophages and vasculature. This approach, initially aimed at uncovering tumor invasive patterns and their interactions with the TME, holds promise for understanding additional cancer cell behaviors like intravasation and metastasis. BEHAV3D-TP represents a significant advancement in democratizing the analysis of heterogeneous cancer cell behaviors and their TME interactions, providing accessible computational insights into tumor dynamics.

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CellTracksColab — A platform for compiling, analyzing, and exploring tracking data

Cited by 5 publications

References 45 publications

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

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

Application of tissue-scale tension to avian epitheliain vivoto study multiscale mechanics and inter-germ layer coupling

BEHAV3D Tumor Profiler to map heterogeneous cancer cell behavior in the tumor microenvironment

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