Titre :

Robust cell tracking in epithelial tissues through identification of maximum common subgraphs

Auteur(s) :

Kursawe, Jochen [Auteur]
Mathematical Institute [Oxford] [MI]
Bardenet, Remi [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Centre National de la Recherche Scientifique [CNRS]
Zartman, Jeremiah [Auteur]
Department of Chemical and Biomolecular Engineering (Notre Dame, USA)
Fletcher, Alexander [Auteur]
School of Mathematics and Statistics [Sheffield] [SoMaS]
Baker, Ruth [Auteur]

Titre de la revue :

Journal of The Royal Society Interface

Éditeur :

the Royal Society

Date de publication :

2016-11-09

ISSN :

1742-5689

Mot(s)-clé(s) en anglais :

biomedical engineering
biomathematics
cell tracking
planar graphs
maximum
common subgraph
epithelial sheets

Discipline(s) HAL :

Mathématiques [math]/Statistiques [math.ST]

Résumé en anglais : [en]

Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and ...
Lire la suite >Tracking of cells in live-imaging microscopy videos of epithelial sheets is a powerful tool for investigating fundamental processes in embryonic development. Characterizing cell growth, proliferation, intercalation and apoptosis in epithelia helps us to understand how morphogenetic processes such as tissue invagination and extension are locally regulated and controlled. Accurate cell tracking requires correctly resolving cells entering or leaving the field of view between frames, cell neighbour exchanges, cell removals and cell divisions. However, current tracking methods for epi-thelial sheets are not robust to large morphogenetic deformations and require significant manual interventions. Here, we present a novel algorithm for epithelial cell tracking, exploiting the graph-theoretic concept of a 'maxi-mum common subgraph' to track cells between frames of a video. Our algorithm does not require the adjustment of tissue-specific parameters, and scales in sub-quadratic time with tissue size. It does not rely on precise positional information, permitting large cell movements between frames and enabling tracking in datasets acquired at low temporal resolution due to experimental constraints such as phototoxicity. To demonstrate the method, we perform tracking on the Drosophila embryonic epidermis and compare cell–cell rearrangements to previous studies in other tissues. Our implementation is open source and generally applicable to epithelial tissues.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Projet ANR :

Inférence bayésienne à ressources limitées - données massives et modèles coûteux

Collections :

• Centre de Recherche en Informatique, Signal et Automatique de Lille (CRIStAL) - UMR 9189

Source :

Harvested from HAL