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.

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。