FIEVET, G. | Search bioRxiv

bioRxiv2026

Scalable expansion of human iNKT cells: single-cell profiling and in vivo control of GvHD with preserved GvL activity

Invariant natural killer T (iNKT) cells can limit graft-versus-host disease (GVHD) after hematopoietic stem cell transplantation (HSCT), but their scarcity in peripheral blood limitstherapeutic development. Current clinical-grade human iNKT expansion protocols mainly rely on IL-2, require prior iNKT-cell sorting, last 6-8 weeks, and predominantly expand CD4+ iNKT cells, whereas human CD4- iNKT cells are more strongly associated with GVHD control in patients and uniquely regulate antigen-presenting cells and T-cell activation. We developed a scalable culture system to preferentially expand human CD4- iNKT cells directly from total peripheral blood mononuclear cells (PBMCs) using alpha galactosylceramide (-GalCer) and optimized cytokine conditions. IL-15 was the most effective cytokine. The optimized 14-day protocol generated a mean of 3.8x107 iNKT cells from 2x107 PBMCs, including 74% CD4- iNKT cells. Single-cell transcriptomic profiling identified eight major iNKT subsets, differentiation trajectories during expansion, and distinct IL-2- versus IL-15-associated transcriptional programs. IL-15-expanded iNKT cells induced apoptosis of monocyte-derived dendritic and leukemic cells in vitro, controlled xeno-GVHD, and preserved graft-versus-leukemia (GVL) activity in preclinical mouse models. This platform enables reproducible production of human CD4- iNKT cells at clinically relevant scale and position IL-15-expanded iNKT cells as a compelling immunotherapy candidate for allo-HSCT.

immunology↗

bioRxiv2025

The eXplainable Artificial Intelligence (XAI) Triad: Models, Importances, and Significance at Scale

In this study, we present a comprehensive evaluation framework for comparing various combinations of artificial intelligence (AI) methods in the context of explainable AI (XAI) for variable selection in experimental biological and biomedical data. Our goal was to assess the efficiency, computational cost, and accuracy of different method combinations across six simulated scenarios, each replicated ten times. These scenarios encompass various classification and regression complexities, including variance differences, bimodal distributions, eXclusive-OR (XOR) interactions, concentric circles, and nonlinear relationships such as parabolic and sinusoidal functions. We tested several machine learning algorithms, including Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP). We combined these models with diverse feature-importance methods such as Gini importance, accuracy decrease, SHAP values (Shapley Additive exPlanations), and Oldens method. We further applied significance-thresholding approaches, namely PIMP (Permutation IMPortance), mProbes, and the novel simThresh developed for this study. Additionally, we explored different dataset sizes to evaluate the scalability of these methods. Our analysis revealed substantial differences in computational demands, ranging from very rapid evaluations (e.g., DT combined with Gini importance and simThresh averaging 0.15 seconds) to extensive computations (e.g., MLP combined with SHAP and PIMP exceeding 7 hours). Among the tested combinations, RF/Accuracy/PIMP achieved the best overall performance, successfully identifying 59 out of 60 replicates in our benchmark study. However, this approach raises concerns regarding its scalability when applied to large-scale omics datasets in real-world settings due to its computational demands. In contrast, Decision Tree or Random Forest models using Gini and simThresh criteria ranked second, with 50 out of 60 detections. While less accurate, these methods require fewer computational resources, making them more promising candidates for scalable applications in omics data analysis. The proposed evaluation framework thus serves as a valuable tool for method selection, particularly relevant when dealing with large-scale omics datasets where computational resources and accuracy are both critical considerations.

bioinformatics↗