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 prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering, and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC dataset of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro, linking morphological features with function and demonstrating the potential to significantly impact antibody design. scifAI is universally applicable to IFC data. Given its modular architecture, it is straightforward to incorporate into existing workflows and analysis pipelines, e.g., for rapid antibody screening and functional characterization.
© 2023. The Author(s).

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 prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC data set of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency- and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro, linking morphological features with function and demonstrating the potential to significantly impact antibody design. 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.

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 prediction of function is lacking. Here, we introduce the first comprehensive open-source framework, scifAI (single-cell imaging flow cytometry AI), for preprocessing, feature engineering and explainable, predictive machine learning on imaging flow cytometry (IFC) data. Additionally, we generate the largest publicly available IFC data set of the human immunological synapse containing over 2.8 million images. Using scifAI, we analyze class frequency- and morphological changes under different immune stimulation. T cell cytokine production across multiple donors and therapeutic antibodies is quantitatively predicted in vitro , linking morphological features with function and demonstrating the potential to significantly impact antibody design. 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.

Mantle cell lymphoma (MCL) is a malignancy arising from naive B lymphocytes with common bone marrow (BM) involvement. Although t(11;14) is a primary event in MCL development, the highly diverse molecular etiology and causal genomic events are still being explored. We investigated the transcriptome of CD19+ BM cells from eight MCL patients at single-cell level. The transcriptomes revealed marked heterogeneity across patients, while general homogeneity and clonal continuity was observed within the patients with no clear evidence of subclonal involvement. All patients were SOX11+CCND1+CD20+. Despite monotypic surface immunoglobulin (Ig) κ or λ protein expression in MCL, 10.9% of the SOX11 + malignant cells expressed both light chain transcripts. The early lymphocyte transcription factor SOX4 was expressed in a fraction of SOX11 + cells in two patients and co-expressed with the precursor lymphoblastic marker, FAT1, in a blastoid case, suggesting a potential prognostic role. Additionally, SOX4 was found to identify non-malignant SOX11- pro-/pre-B cell populations. Altogether, the observed expression of markers such as SOX4, CD27, IgA and IgG in the SOX11+ MCL cells, may suggest that the malignant cells are not fixed in the differentiation state of naïve mature B cells, but instead the patients carry B lymphocytes of different differentiation stages.
© 2021. The Author(s).

The CD8+ T cell response to an antigen is composed of many T cell clones with unique T cell receptors, together forming a heterogeneous repertoire of effector and memory cells. How individual T cell clones contribute to this heterogeneity throughout immune responses remains largely unknown. In this study, we longitudinally track human CD8+ T cell clones expanding in response to yellow fever virus (YFV) vaccination at the single-cell level. We observed a drop in clonal diversity in blood from the acute to memory phase, suggesting that clonal selection shapes the circulating memory repertoire. Clones in the memory phase display biased differentiation trajectories along a gradient from stem cell to terminally differentiated effector memory fates. In secondary responses, YFV- and influenza-specific CD8+ T cell clones are poised to recapitulate skewed differentiation trajectories. Collectively, we show that the sum of distinct clonal phenotypes results in the multifaceted human T cell response to acute viral infections.
Copyright © 2021 The Author(s). Published by Elsevier Inc. All rights reserved.