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..., Cell line culture
EBV-transformed B-lymphoblastoid cell line (B-LCL) from donor 333 was obtained from Astarte Biologics (# 1038-3161JN16) and cells were cultivated in RPMI-1640 medium (PAN-Biotech; cat # P04-17500) with 10% FBS (Anprotec; cat # AC-SM-0014Hi) and 2 mM L-glutamine (PAN-Biotech; cat# P04-80100).
Immune synapse formation and imaging flow cytometry

To analyze immune synapses, human memory CD4+ T cells were isolated from PBMCs of nine healthy human donors using a negative selection EasySep Enrichment kit from STEMCELL Technologies (cat #19157). Live/dead staining of T and B-LCL cells was separately performed using the fixable viability dye eF780 for 15 min at RT (eBioscience; cat # 65-0865-14). Cells were then re-suspended in RPMI-1640 medium supplemented with 10% FBS (Anprotec; cat # AC-SM-0014Hi), 5% Penicillin-Streptomycin (Gibco; cat # 15140-122) and 2 mM L-glutamine (PAN-Biotech; cat # P04-80100). Afterward, B-LCL cells were transferred into a well of a 96-well round b..., Each image is saved as a `.h5` file. It includes these keys: `image`, `mask`. In case there is a label available, the `label` is provided as `str`. The image is a 16-bit image with one bright field channel and multiple fluorescent channels. The mask is the corresponding segmentation for each channel.
Additionally, each file name includes a number, which is the object number from the IFC experiments. Apart from each file, the data comes from four experiments, nine donors, and these conditions: -SEA, +SEA, Teplizumab, CD19-TCB, and CD20-TCB.
```bash
data_path/Experiment_1/Donor_1/-SEA/*.h5
data_path/Experiment_1/Donor_1/-SEA /*.h5
.
.
.
data_path/Experiment_1/Donor_2/-SEA /*.h5
data_path/Experiment_1/Donor_2/-SEA /*.h5
.
.
.
data_path/Experiment_4/Donor_M/CD20-TCB./*.h5
 
```
 
For the feature extraction, you first need to calculate the `metadata` data frame by providing the correct data path. 
 
```python
import scifAI
 
data_path = <PATH TO THE DATA FOLDER>
metadata = scifAI.metad...