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

Agliari, Elena; Sáez, Pablo J.; Barra, Adriano; Piel, Matthieu; Vargas, Pablo Agustín; Castellana, Michele

doi:10.1126/sciadv.aay2103

Cited by 10 publications

(11 citation statements)

References 35 publications

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“…[20] where the large deviation theory of time-averaged statistics [21] was used to find some stochastic properties of measured trajectories, Ref. [22] where a statistical inference approach designed to find interactions between moving particles was used in experimental data, and Ref. [23,24] where a method based in the information contained in the power spectral density of a single trajectory was proposed (for reviews and other methods see also [25][26][27]).…”

Section: Introductionmentioning

confidence: 99%

Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories

Garibo‐i‐Orts¹,

Baeza-Bosca²,

García-March³

et al. 2021

J. Phys. A: Math. Theor.

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Anomalous diffusion occurs at very different scales in nature, from atomic systems to motions in cell organelles, biological tissues or ecology, and also in artificial materials, such as cement. Being able to accurately measure the anomalous exponent associated to a given particle trajectory, thus determining whether the particle subdiffuses, superdiffuses or performs normal diffusion, is of key importance to understand the diffusion process. Also it is often important to trustingly identify the model behind the trajectory, as it this gives a large amount of information on the system dynamics. Both aspects are particularly difficult when the input data are short and noisy trajectories. It is even more difficult if one cannot guarantee that the trajectories output in experiments are homogeneous, hindering the statistical methods based on ensembles of trajectories. We present a data-driven method able to infer the anomalous exponent and to identify the type of anomalous diffusion process behind single, noisy and short trajectories, with good accuracy. This model was used in our participation in the anomalous diffusion (AnDi) challenge. A combination of convolutional and recurrent neural networks was used to achieve state-of-the-art results when compared to methods participating in the AnDi challenge, ranking top 4 in both classification and diffusion exponent regression.

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Section: Introductionmentioning

confidence: 99%

Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories

Garibo‐i‐Orts¹,

Baeza-Bosca²,

García-March³

et al. 2021

J. Phys. A: Math. Theor.

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“…Using a mathematical model, Agliari et al. explored the hypothesis that ATP release and autocrine signaling during immune cell migration might impact neighboring cells while migrating as a group of single cells ( 140 ). The statistical inference approach revealed that migrating DCs have no instantaneous cell communication via release of small soluble molecules, such as ATP ( 140 ).…”

Section: Discussionmentioning

confidence: 99%

“…explored the hypothesis that ATP release and autocrine signaling during immune cell migration might impact neighboring cells while migrating as a group of single cells ( 140 ). The statistical inference approach revealed that migrating DCs have no instantaneous cell communication via release of small soluble molecules, such as ATP ( 140 ). However, the model only predicts immediate interactions and the release of adenine nucleotides or other small molecules will act with a delay considering the diffusion time and other parameters.…”

Section: Discussionmentioning

confidence: 99%

Pannexin Channel Regulation of Cell Migration: Focus on Immune Cells

et al. 2021

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The role of Pannexin (PANX) channels during collective and single cell migration is increasingly recognized. Amongst many functions that are relevant to cell migration, here we focus on the role of PANX-mediated adenine nucleotide release and associated autocrine and paracrine signaling. We also summarize the contribution of PANXs with the cytoskeleton, which is also key regulator of cell migration. PANXs, as mechanosensitive ATP releasing channels, provide a unique link between cell migration and purinergic communication. The functional association with several purinergic receptors, together with a plethora of signals that modulate their opening, allows PANX channels to integrate physical and chemical cues during inflammation. Ubiquitously expressed in almost all immune cells, PANX1 opening has been reported in different immunological contexts. Immune activation is the epitome coordination between cell communication and migration, as leukocytes (i.e., T cells, dendritic cells) exchange information while migrating towards the injury site. In the current review, we summarized the contribution of PANX channels during immune cell migration and recruitment; although we also compile the available evidence for non-immune cells (including fibroblasts, keratinocytes, astrocytes, and cancer cells). Finally, we discuss the current evidence of PANX1 and PANX3 channels as a both positive and/or negative regulator in different inflammatory conditions, proposing a general mechanism of these channels contribution during cell migration.

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“…Despite its simplicity, this elementary model leads to very interesting emergent behavior and generates quite natural synthetic trajectories. , Examples can be found in Figure c,d.…”

Section: Methods B: Computational Protocolsmentioning

confidence: 99%

Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors

Chandra

Prasad

Alemanno

et al. 2022

ACS Appl. Mater. Interfaces

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pH balance and regulation within organelles are fundamental to cell homeostasis and proliferation. The ability to track pH in cells becomes significantly important to understand these processes in detail. Fluorescent sensors based on micro- and nanoparticles have been applied to measure intracellular pH; however, an accurate methodology to precisely monitor acidification kinetics of organelles in living cells has not been established, limiting the scope of this class of sensors. Here, silica-based fluorescent microparticles were utilized to probe the pH of intracellular organelles in MDA-MB-231 and MCF-7 breast cancer cells. In addition to the robust, ratiometric, trackable, and bioinert pH sensors, we developed a novel dimensionality reduction algorithm to automatically track and screen massive internalization events of pH sensors. We found that the mean acidification time is comparable among the two cell lines (ΔT MCF‑7 = 16.3 min; ΔT MDA‑MB‑231 = 19.5 min); however, MCF-7 cells showed a much broader heterogeneity in comparison to MDA-MB-231 cells. The use of pH sensors and ratiometric imaging of living cells in combination with a novel computational approach allow analysis of thousands of events in a computationally inexpensive and faster way than the standard routes. The reported methodology can potentially be used to monitor pH as well as several other parameters associated with endocytosis.

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

Cited by 10 publications

References 35 publications

Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories

Efficient recurrent neural network methods for anomalously diffusing single particle short and noisy trajectories

Pannexin Channel Regulation of Cell Migration: Focus on Immune Cells

Fully Automated Computational Approach for Precisely Measuring Organelle Acidification with Optical pH Sensors

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