Artificial intelligence in hematopoietic stem cell research and associated malignancies: From disease modeling to cell manufacturing

Abstract

Hematopoietic stem cells (HSCs) occupy the apex of the blood cell hierarchy, and artificial intelligence (AI) is fundamentally reshaping how their biology is decoded from normal self-renewal to malignant transformation and clinical transplantation. Trajectory inference algorithms applied to single-cell multi-omics have resolved continuous HSC differentiation with lineage priming detectable at the single-cell level, while convolutional neural networks trained on chromatin imaging predict HSC biological age and detect epigenetic rejuvenation signatures. In leukemic stem cell (LSC) biology, multi-omics deep learning models map treatment-resistant quiescent LSC subclones, detect minimal residual disease with an area under the curve of 0.97 and predict venetoclax sensitivity in LSC-enriched niches. Deep learning also maps myeloma stem cell niche interactions and spatial heterogeneity in bone marrow biopsies. Virtual screening powered by AI speeds up LSC-targeted drug discovery, and reinforcement learning and digital twin models improve ex vivo HSC manufacturing and industrial-scale production of chimeric antigen receptor-T cells. In transplantation, natural language processing extraction and hybrid models classify risk for graft-vs-host disease into clinically relevant subgroups. Overall, top-performing AI models serve as computational surrogates for stemness biology but challenges in dataset diversity, interpretability and regulatory compliance need to be addressed before being used in a clinical setting.

Keywords: Artificial intelligence; Hematopoietic stem cells; Machine learning; Deep learning; Drug discovery; Biological products; Automation; Acute myeloid leukemia; Multiple myeloma

Core Tip: Artificial intelligence is redefining the study of hematopoietic stem cells (HSCs) by serving as a computational guide to stemness biology. Machine learning and deep learning delineate HSC fate trajectories, unravel age-dependent HSC self-renewal, and identify enriched leukemic stem cell sub-compartments that sustain treatment-resistant leukemia and relapse in acute myeloid leukemia and multiple myeloma. The same approaches expedite leukemic stem cell-targeted drug discovery, model HSC biomanufacturing for digital twins and predict transplant risk using natural language processing. High-performing artificial intelligence models are therefore quantitative surrogates for stem cell state - and span single-cell biology and decision-making across the HSC continuum.