最近的研究指出，细胞群在其环境中的动态和相互作用与免疫学中的几个生物过程有关。因此，免疫学中的单细胞分析现在依赖于空间组学。此外，最近的文献表明，免疫学场景是分层组织的，包括在一些可观察的对照组和治疗组中以不同比例出现的未知细胞行为。这些动态行为在识别炎症、衰老和抵抗病原体或癌细胞等过程的原因方面发挥着至关重要的作用。在这项工作中，我们使用自我监督的学习方法来发现免疫学场景中与细胞动力学相关的这些行为。具体来说，我们研究了对照组和治疗组在涉及梗死炎症的场景中的不同反应，重点是影响中性粒细胞在血管内的迁移。从一组手工制作的时空特征开始，我们使用循环神经网络来生成正确描述迁移过程动态的嵌入。该网络使用一种新颖的多任务对比损失进行训练，一方面对场景的层次结构（组-行为-样本）进行建模，另一方面确保嵌入内的时间一致性，强制后续的时间一致性从给定细胞获得的样本在潜在空间中保持紧密。我们的实验结果表明，与手工制作的特征提取和状态的最新方法相比，由此产生的嵌入提高了细胞行为的可分离性和治疗的对数似然性即使降维（16 个与 21 个手工制作的特征），我们的方法也能够在群体水平上进行单细胞分析，能够自动发现不同群体之间的共同行为。反过来，这可以根据研究组中的比例来预测治疗效果。版权所有 © 2024 作者。由 Elsevier B.V. 出版。保留所有权利。

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.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.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).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.Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.