目的:最近的研究指出,细胞种群在其环境中的动力学和相互作用与免疫学中的几个生物学过程有关。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,免疫学情景是分层组织的,包括一些可观察到的对照组和治疗组以不同比例出现的未知细胞行为。这些动态行为在确定炎症等过程的原因中起着至关重要的作用。老化,对抗病原体或癌细胞。在这项工作中,我们使用自监督学习方法来发现这些与免疫学场景中的细胞动力学相关的行为。
方法:具体来说,我们研究了对照组和治疗组在梗死引起炎症的情况下的不同反应,关注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,我们使用递归神经网络来生成嵌入,以正确描述迁移过程的动态。该网络使用一种新颖的多任务对比损失进行训练,一方面,对我们的场景(群体-行为-样本)的层次结构进行建模,另一方面,确保嵌入内的时间一致性,强制从给定细胞获得的后续时间样本在潜在空间中保持接近。
结果:我们的实验结果表明,所产生的嵌入提高了细胞行为的可分性和治疗的对数可能性,与手工制作的特征提取和最新的最新方法相比,即使降维(16vs.21个手工制作的功能)。
结论:我们的方法可以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享行为。这个,反过来,可以根据研究组中的比例来预测治疗效果。
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.
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.