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Published on: April 4, 2013
A self-supervised embedding of cell migration features for behavior discovery over cell populations
Miguel Molina-Moreno1, Iván González-Díaz2, Ralf Mikut3
1Department of Signal Theory and Communications, Universidad Carlos III de Madrid, Avda. de la Universidad, 30, Leganés, 28911, Spain; Department of Immunobiology, Yale University, Amistad Street Building, 10 Amistad St, New Haven, 06520, USA.
Insights
This study introduces a self-supervised learning method to uncover hidden cell behaviors in immunology. The approach enhances single-cell analysis by identifying therapy effectiveness through cell population dynamics.
Area of Science:
- Immunology
- Computational Biology
- Systems Biology
Background:
- Cell population dynamics are crucial for understanding immunological processes like inflammation and disease.
- Spatial omics and single-cell analysis are key tools in modern immunology research.
- Hierarchical organization of immunological scenarios reveals distinct cell behaviors across different groups.
Purpose of the Study:
- To develop a self-supervised learning approach for discovering cell dynamics and behaviors in immunology.
- To analyze neutrophil migration in an infarct inflammation model.
- To model hierarchical structures and temporal consistency in cell behavior analysis.
Main Methods:
- Utilized a recurrent neural network with hand-crafted spatio-temporal features.
- Employed a novel multi-task contrastive loss for training.
- Focused on modeling hierarchical organization (groups-behaviors-samples) and temporal consistency.
Main Results:
- Generated embeddings that improve cell behavior separability and therapy log-likelihood.
- Outperformed traditional feature extraction and state-of-the-art methods.
- Achieved better results with reduced dimensionality (16 features vs. 21).
Conclusions:
- The method enables population-level single-cell analysis by automatically discovering shared cell behaviors.
- It facilitates prediction of therapy effectiveness based on the proportions of discovered behaviors.
- This approach advances understanding of cell dynamics in complex biological systems.
Background And Objective:
Recent studies point out that the dynamics and interaction of cell populations within their environment are related to several biological processes in immunology. Hence, single-cell analysis in immunology now relies on spatial omics. Moreover, recent literature suggests that immunology scenarios are hierarchically organized, including unknown cell behaviors appearing in different proportions across some observable control and therapy groups. These dynamic behaviors play a crucial role in identifying the causes of processes such as inflammation, aging, and fighting off pathogens or cancerous cells. In this work, we use a self-supervised learning approach to discover these behaviors associated with cell dynamics in an immunology scenario.
Materials And Methods:
Specifically, we study the different responses of control group and therapy groups in a scenario involving inflammation due to infarct, with a focus on neutrophil migration within blood vessels. Starting from a set of hand-crafted spatio-temporal features, we use a recurrent neural network to generate embeddings that properly describe the dynamics of the migration processes. The network is trained using a novel multi-task contrastive loss that, on the one hand, models the hierarchical structure of our scenario (groups-behaviors-samples) and, on the other, ensures temporal consistency within the embedding, enforcing that subsequent temporal samples obtained from a given cell stay close in the latent space.
Results:
Our experimental results demonstrate that the resulting embeddings improve the separability of cell behaviors and log-likelihood of the therapies, when compared to the hand-crafted feature extraction and recent methods from the state of the art, even with dimensionality reduction (16 vs. 21 hand-crafted features).
Conclusions:
Our approach enables single-cell analyses at a population level, being able to automatically discover shared behaviors among different groups. This, in turn, enables the prediction of the therapy effectiveness based on their proportions within a study group.
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