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A statistical inference approach to reconstruct intercellular interactions in cell migration experiments

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A statistical inference approach to reconstruct intercellular interactions in cell migration experiments. / Agliari, Elena; Sáez, Pablo J; Barra, Adriano; Piel, Matthieu; Vargas, Pablo; Castellana, Michele.

In: SCI ADV, Vol. 6, No. 11, eaay2103, 03.2020.

Research output: SCORING: Contribution to journal › SCORING: Journal article › Research › peer-review

Harvard

Agliari, E, Sáez, PJ, Barra, A, Piel, M, Vargas, P & Castellana, M 2020, 'A statistical inference approach to reconstruct intercellular interactions in cell migration experiments', SCI ADV, vol. 6, no. 11, eaay2103. https://doi.org/10.1126/sciadv.aay2103

APA

Agliari, E., Sáez, P. J., Barra, A., Piel, M., Vargas, P., & Castellana, M. (2020). A statistical inference approach to reconstruct intercellular interactions in cell migration experiments. SCI ADV, 6(11), [eaay2103]. https://doi.org/10.1126/sciadv.aay2103

Vancouver

Agliari E, Sáez PJ, Barra A, Piel M, Vargas P, Castellana M. A statistical inference approach to reconstruct intercellular interactions in cell migration experiments. SCI ADV. 2020 Mar;6(11). eaay2103. https://doi.org/10.1126/sciadv.aay2103

Bibtex

@article{9f5a5df164dc40c9ab92374797c0c9b2,
title = "A statistical inference approach to reconstruct intercellular interactions in cell migration experiments",
abstract = "Migration of cells can be characterized by two prototypical types of motion: individual and collective migration. We propose a statistical inference approach designed to detect the presence of cell-cell interactions that give rise to collective behaviors in cell motility experiments. This inference method has been first successfully tested on synthetic motional data and then applied to two experiments. In the first experiment, cells migrate in a wound-healing model: When applied to this experiment, the inference method predicts the existence of cell-cell interactions, correctly mirroring the strong intercellular contacts that are present in the experiment. In the second experiment, dendritic cells migrate in a chemokine gradient. Our inference analysis does not provide evidence for interactions, indicating that cells migrate by sensing independently the chemokine source. According to this prediction, we speculate that mature dendritic cells disregard intercellular signals that could otherwise delay their arrival to lymph vessels.",
keywords = "Animals, Cell Communication, Cell Movement, Dendritic Cells/metabolism, HeLa Cells, Humans, Mice, Models, Biological, Wound Healing",
author = "Elena Agliari and S{\'a}ez, {Pablo J} and Adriano Barra and Matthieu Piel and Pablo Vargas and Michele Castellana",
note = "Copyright {\textcopyright} 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).",
year = "2020",
month = mar,
doi = "10.1126/sciadv.aay2103",
language = "English",
volume = "6",
journal = "SCI ADV",
issn = "2375-2548",
publisher = "American Association for the Advancement of Science",
number = "11",

}

RIS

TY - JOUR

T1 - A statistical inference approach to reconstruct intercellular interactions in cell migration experiments

AU - Agliari, Elena

AU - Sáez, Pablo J

AU - Barra, Adriano

AU - Piel, Matthieu

AU - Vargas, Pablo

AU - Castellana, Michele

N1 - Copyright © 2020 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

PY - 2020/3

Y1 - 2020/3

N2 - Migration of cells can be characterized by two prototypical types of motion: individual and collective migration. We propose a statistical inference approach designed to detect the presence of cell-cell interactions that give rise to collective behaviors in cell motility experiments. This inference method has been first successfully tested on synthetic motional data and then applied to two experiments. In the first experiment, cells migrate in a wound-healing model: When applied to this experiment, the inference method predicts the existence of cell-cell interactions, correctly mirroring the strong intercellular contacts that are present in the experiment. In the second experiment, dendritic cells migrate in a chemokine gradient. Our inference analysis does not provide evidence for interactions, indicating that cells migrate by sensing independently the chemokine source. According to this prediction, we speculate that mature dendritic cells disregard intercellular signals that could otherwise delay their arrival to lymph vessels.

AB - Migration of cells can be characterized by two prototypical types of motion: individual and collective migration. We propose a statistical inference approach designed to detect the presence of cell-cell interactions that give rise to collective behaviors in cell motility experiments. This inference method has been first successfully tested on synthetic motional data and then applied to two experiments. In the first experiment, cells migrate in a wound-healing model: When applied to this experiment, the inference method predicts the existence of cell-cell interactions, correctly mirroring the strong intercellular contacts that are present in the experiment. In the second experiment, dendritic cells migrate in a chemokine gradient. Our inference analysis does not provide evidence for interactions, indicating that cells migrate by sensing independently the chemokine source. According to this prediction, we speculate that mature dendritic cells disregard intercellular signals that could otherwise delay their arrival to lymph vessels.

KW - Animals

KW - Cell Communication

KW - Cell Movement

KW - Dendritic Cells/metabolism

KW - HeLa Cells

KW - Humans

KW - Mice

KW - Models, Biological

KW - Wound Healing

U2 - 10.1126/sciadv.aay2103

DO - 10.1126/sciadv.aay2103

M3 - SCORING: Journal article

C2 - 32195344

VL - 6

JO - SCI ADV

JF - SCI ADV

SN - 2375-2548

IS - 11

M1 - eaay2103

ER -