An imaging flow cytometry dataset for profiling the immunological synapse of therapeutic antibodies

Therapeutic antibodies are widely used to treat severe diseases. Most of them alter immune cells and act within the immunological synapse, an essential cell-to-cell interaction to direct the humoral immune response. Although many antibody designs are generated and evaluated, a high-throughput tool for systematic antibody characterization and function prediction is lacking. Here, we generate the largest publicly available imaging flow cytometry (IFC) data set of the human immunological synapse containing over 2.8 million images. This dataset is used to analyze class frequency and morphological changes under different immune stimulation. In addition to the dataset, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI, https://github.com/marrlab/scifAI), for preprocessing, feature engineering, and explainable, predictive machine learning IFC data. Using scifAI, we analyze class frequency- and morphological changes under different immune stimulation. scifAI is universally applicable to IFC data and, given its modular architecture, straightforward to incorporate into existing workflows and analysis pipelines, e.g., for rapid antibody screening and functional characterization.治疗性抗体（Therapeutic antibodies）被广泛应用于重症疾病的治疗。其中多数可作用于免疫细胞，并在免疫突触（immunological synapse）内发挥功能——免疫突触是指导体液免疫应答的关键细胞间相互作用结构。尽管目前已生成并评估了众多抗体设计方案，但仍缺乏可用于系统性抗体表征与功能预测的高通量工具。本研究构建了目前规模最大的公开可用人类免疫突触成像流式细胞术（imaging flow cytometry, IFC）数据集，包含超过280万张图像。该数据集可用于分析不同免疫刺激条件下的细胞类群频率与形态学变化。除数据集外，本研究还推出了首个综合性开源框架scifAI（单细胞成像流式细胞术AI，single-cell imaging flow cytometry AI，https://github.com/marrlab/scifAI），可用于IFC数据的预处理、特征工程以及可解释性预测机器学习分析。借助scifAI框架，我们可分析不同免疫刺激条件下的细胞类群频率与形态学变化。scifAI可广泛适配各类IFC数据，且由于其模块化架构，能够轻松集成至现有工作流与分析流程中，例如用于快速抗体筛选与功能表征。

搜集汇总

背景与挑战

背景概述

该数据集是目前公开可用的最大的人类免疫突触成像流式细胞术数据集，包含超过280万张图像，总大小约124.94GB，用于分析治疗性抗体在不同免疫刺激下的类别频率和形态变化。数据集与开源机器学习框架scifAI配套，支持特征提取和可解释的预测分析，适用于抗体筛选和功能表征研究。