DynaCLR：利用时间正则化对细胞动力学进行对比学习
DynaCLR: Contrastive Learning of Cellular Dynamics with Temporal Regularization
AI总结：
DynaCLR提出一种结合单细胞跟踪与时间感知对比采样的自监督方法，学习时间正则化的细胞动力学表示，支持细胞分裂/感染分类、跨模态蒸馏及细胞器反应发现等多种下游任务。
摘要：
我们报告了DynaCLR，这是一种自监督方法，通过对延时图像的表示进行对比学习来嵌入细胞和细胞器动力学。DynaCLR整合了单细胞跟踪和时间感知对比采样，以学习稳健的、时间正则化的细胞动力学表示。DynaCLR嵌入能够有效泛化到分布内和分布外数据集，并可用于多个具有稀疏人工标注的下游任务。我们展示了利用荧光和无标记成像通道，通过人在回路的细胞状态高效标注。DynaCLR方法支持多种下游生物学分析：细胞分裂和感染的分类、异质细胞迁移模式的聚类、从荧光通道到无标记通道的细胞状态跨模态蒸馏、异步细胞反应和断裂细胞轨迹的对齐，以及发现由感染引起的细胞器反应。DynaCLR是一种灵活的方法，用于对药物、微生物和遗传扰动引起的动态细胞反应进行比较分析。我们提供了基于PyTorch的模型训练和推理流程实现（https://github.com/mehta-lab/viscy）以及一个GUI（https://github.com/czbiohub-sf/napari-iohub），用于在真实空间和嵌入空间中可视化和标注细胞轨迹。
英文摘要：
We report DynaCLR, a self-supervised method for embedding cell and organelle Dynamics via Contrastive Learning of Representations of time-lapse images. DynaCLR integrates single-cell tracking and time-aware contrastive sampling to learn robust, temporally regularized representations of cell dynamics. DynaCLR embeddings generalize effectively to in-distribution and out-of-distribution datasets, and can be used for several downstream tasks with sparse human annotations. We demonstrate efficient annotations of cell states with a human-in-the-loop using fluorescence and label-free imaging channels. DynaCLR method enables diverse downstream biological analyses: classification of cell division and infection, clustering heterogeneous cell migration patterns, cross-modal distillation of cell states from fluorescence to label-free channel, alignment of asynchronous cellular responses and broken cell tracks, and discovering organelle response due to infection. DynaCLR is a flexible method for comparative analyses of dynamic cellular responses to pharmacological, microbial, and genetic perturbations. We provide PyTorch-based implementations of the model training and inference pipeline (https://github.com/mehta-lab/viscy) and a GUI (https://github.com/czbiohub-sf/napari-iohub) for the visualization and annotation of trajectories of cells in the real space and the embedding space.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
