Cell-based therapies of autoimmune diseases in the context of artificial intelligence development

Vollständiger Abstract

Worum geht es in dieser Arbeit?

Abstract Autoimmune diseases are comprised of several chronic inflammatory conditions characterized by activation of autoreactive T and B lymphocytes and produces autoantibodies. While most of the current treatment options mainly rely on non-specific immunosuppression, stem cell based therapies can be a promising candidate for promoting “immune reset” and restore long term tolerance against these complex diseases. Stem cell based therapy eliminate autoreactive clones and reconstitute immune tolerance. Besides, mesenchymal stromal cells (MSCs; also referred to as mesenchymal stem cells) have immunomodulatory effects via paracrine signalling, including anti-inflammatory cytokine and extracellular vesicles secretion. However, successful clinical translation is limited by complex high-dimensional datasets and patient heterogeneity. In this review, we have focused on the convergent journey of Artificial Intelligence (AI) and stem cell based therapies, which mainly evolved in recent times. We also discussed how machine learning and deep learning methods facilitate the analysis of single-cell multi-omics, spatial transcriptomics, and easily identify disease states. Furthermore, the role of AI in optimizing the design and potency of therapeutic modalities, including regulatory T cells (Tregs), Chimeric Antigen Receptor (CAR)-T cells, and mesenchymal stromal cells (MSCs) have also been explored. Altogether, this review highlights the emerging role of AI-guided approaches in bridging mechanistic immunology with future precision stem cell therapies, while several computational and preclinical studies are promising, prospective clinical validation remains necessary before widespread clinical implementation .