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

Table 1 Stem-cell relevance map linking each application area to stem-cell biological object, data modality, AI methodology, output, clinical/biological endpoint, and validation status

Application	Stem-cell object	Data modality	AI method/model	Dataset/scale	Key performance metrics	Clinical/biological endpoint	Validation status	Ref.
A: Normal HSC biology
HSC differentiation modeling	Normal HSCs, MPPs	scRNA-seq, scATAC-seq	VIA (Voyager) - lazy-teleporting MCMC trajectory inference	Human CD34+ hematopoiesis (multi-site)	F1 > 0.9 for rare lineage populations; robust across scRNA-seq and scATAC-seq	HSC differentiation dynamics; lineage bifurcation mapping	Cross-modal validation (scRNA-seq + scATAC-seq)	[5]
HSC aging - chromatin architecture	Young vs aged murine HSCs	3D confocal DAPI chromatin imaging	ChromAgeNet - CNN on 3D nuclear images	1229 HSC nuclei (551 young, 678 aged)	AUROC: 0.77 ± 0.03; accuracy 0.68 ± 0.05; outperforms handcrafted features (AUROC: 0.73)	Biological age prediction; epigenetic rejuvenation detection	5-fold cross-validation; drug-treatment validation	[6]
HSC division dynamics	HSCs/progenitors - 3 age groups (young, mid-life, aged)	Single-cell gene expression	ANN	9 stem/progenitor populations across 3 age groups	89% accuracy (cell type + donor age); 96% accuracy (regenerative status)	Age-dependent self-renewal; niche sensitivity across lifespan	Cross-validation	[10]
HSC morphological classification	HSCs vs MPPs	Bright-field microscopy images	Deep CNN (morphology-based)	Steady-state bright-field image dataset	High accuracy distinguishing HSCs from MPPs; morphological features encode functional state; > 98% accuracy in leukemic BM context	Non-destructive functional state identification without molecular profiling	Steady-state + leukemic BM validation	[11]
HSC quiescence regulation	LTHSCs, STHSCs	scRNA-seq integrated with niche signals (TPO, SCF, ANGPT1)	Boolean network modeling (constraint programming + model checking)	Pseudotrajectory-derived gene expression states	Predicted stable LTHSC, STHSC, and proliferating states; novel p53-ROS regulatory mechanism identified	Quiescence maintenance; stem cell pool regulation	Experimental validation of predicted mechanisms	[12]
Epigenetic/GRN regulation	HSCs at differentiation stages	scRNA-seq, scATAC-seq	SCENIC (TF network inference); CellOracle (in silico TF perturbation)	Multi-dataset hematopoietic single-cell data	Stage-specific TF activities (GATA1, CEBPD, IRF8) validated by chromatin accessibility	TF programs governing HSC self-renewal and differentiation	Cross-validated in mouse and human hematopoiesis	[13,14]
Multi-omic integration	HSCs/progenitors - incomplete or unpaired multi-omics	scRNA-seq + scATAC-seq + CITE-seq	scMaui (product-of-experts VAE); totalVI (joint RNA + protein latent space)	PBMC CITE-seq benchmarks; human HSPC datasets	Robust batch correction; missing-modality handling; joint RNA + protein embedding	Systems-level HSC state characterization; HSC/LSC surface phenotype + transcriptional integration	Benchmarked on standard CITE-seq datasets; applied to HSPC/LSC contexts	[15,16]
B: AML and LSCs
AML risk stratification	LSC-enriched compartments	Clinical, molecular, cytogenetic, and genomic features	Supervised ML (ensemble methods)	1383 patients - multicenter cohort	Predicted complete remission and 2-year OS; outperformed ELN-only stratification	Prognosis; LSC burden surrogate; treatment-intensity guidance	External validation in an independent cohort	[17]
AML transcriptomic risk/epigenetic subtypes	AML blast/LSC-enriched populations	Transcriptomic + epigenetic multi-omics (multi-center)	ML epigenetic subtype classification; prognostic integration	Multi-center transcriptomic cohort	Prognostic stratification by epigenetic subtype; integration with ELN 2022	Relapse risk; therapeutic vulnerability by epigenetic class	Multi-center validation	[18]
AML drug response prediction - multi-omics ensemble (MDREAM)	AML patient-derived blasts; LSC-enriched compartments	Multi-omics: Genomic mutations + transcriptomic gene expression (ex vivo drug sensitivity)	Ensemble ML (MDREAM framework; multi-omics integration)	BeatAML cohort: 278 training/183 validation; + Swedish AML cohort (n = 45); relapsed/refractory cohort (n = 12)	Spearman r = 0.68 (BeatAML validation); 77% correct responder identification at prediction confidence > 0.75	Drug response prediction in AML patients; therapy selection; resistance identification	External multi-cohort validation (BeatAML + 2 independent cohorts)	[19]
AML targeted therapy - network ML	LSC populations; genomic/proteomic networks	Genomic and proteomic datasets	Network-based machine learning	Multi-dataset genomic + proteomic	Identified resistance pathways and therapeutic vulnerabilities	Immunotherapy target prioritization; resistance mechanism mapping	Not fully specified	[20]
AML MRD detection (MAGIC-DR)	Residual LSC-like subpopulations (immature monocytic cells)	Multiparameter flow cytometry	XGBoost + UMAP (MAGIC-DR framework)	AML MRD specimen cohort	AUC of 0.97; strong concordance with expert manual analysis; identified LSC-like residual populations missed by manual gating	Relapse risk; MRD+/- classification; sub-threshold LSC-like disease detection	Prospective validation cohort	[21]
AML LSC phenotyping/MRD	CD34+CD38- LSC-enriched compartment	Flow cytometry (CD117, CD34, HLA-DR)	Random forest + SHAP explainability	194 patients (AML, AML-CR, normal BM)	Accuracy: 94.92%; AUC: 94.83%; CD117, CD34, HLA-DR top discriminators	LSC detection; early relapse identification; AML vs CR vs normal classification	Validated on a 194-patient clinical cohort	[22]
BCL-2/venetoclax response in LSC	LSC-enriched blasts	scRNA-seq + ex vivo drug sensitivity data	XGBoost	VenEx clinical trial patient samples	Venetoclax response prediction r = 0.71-0.84	LSC apoptotic dependency; treatment selection for LSC eradication	VenEx clinical trial validation	[23]
Menin inhibition - LSC clonal architecture	KMT2A-rearranged/NPM1-mutant LSCs	Single-cell multi-omics (scRNA-seq + scATAC-seq)	DL clonal architecture mapping	Clinical trial and patient-derived samples	Mapped HOXA/MEIS1 program activity; tracked LSC self-renewal disruption under Menin inhibition	LSC self-renewal disruption; Menin inhibitor response monitoring	Clinical trial context	[24,25]
LSC surface target prioritization	LSC populations (CD34+)	Multiparameter flow cytometry (LSC immunophenotyping)	ML ranking/feature prioritization	Multi-dataset LSC surface marker profiling	CD123, CLL-1 identified as top immunotherapy targets vs normal HSC	Immunotherapy target selection; CAR-T/bispecific antibody design	Informed clinical trial design	[26]
Synergistic drug combinations - LSC	Diagnosis/relapse LSC-enriched populations	Paired scRNA-seq + ex vivo drug screens	XGBoost (combination prediction)	Paired diagnosis/relapse patient samples	Synergistic drug pair identification; clinically actionable 2-week turnaround	Personalized relapse therapy; LSC-selective combination design	Flow cytometry validation	[23]
AML explainable AI/precision oncology	AML patients (bulk + LSC-enriched contexts)	Transcriptomic, clinical, molecular	Explainable ML: SHAP, attention, ensemble XAI	Multi-center AML cohorts	Improved clinical interpretability; synergistic drug-response signatures via ensemble XAI	Clinical decision support; treatment selection; regulatory compliance	Multi-center validation	[27-29]
C: MMSCs
MM spatial mapping - bone marrow niche	MMSCs; niche cells (BLIMP1+, CD8+, stromal)	Multiplex-IHC trephine biopsies	DL: MoSaicNet (tissue segmentation) + AwareNet (rare-cell detection)	MGUS and NDMM clinical trephine biopsy cohort	Spatial heterogeneity as key MGUS-to-NDMM distinction; tumor-immune spatial proximity mapped	Sanctuary identification for quiescent MMSCs; immune exclusion mapping; MGUS vs NDMM distinction	Validated on clinical MGUS and NDMM samples	[30]
MMSC niche interactions - treatment resistance	Quiescent MMSCs; BM stromal cells	Multi-omics datasets	Neural networks/DL	Multi-omics patient-derived datasets	Prediction of treatment resistance linked to niche-protective microenvironments	Niche-mediated drug resistance; MMSC persistence	Experimental/in vitro validation (details not fully specified)	[31,32]
D: Drug discovery and LSC-targeted therapy
Virtual screening/AI-enhanced docking	LSC-targeted compounds; kinase/epigenetic targets	Multi-billion-compound chemical libraries (structure-based)	AI-enhanced virtual screening; molecular docking; structure-based drug design	Ultra-large compound libraries	Accelerated hit/Lead identification; 11 confirmed hits validated by X-ray crystallography; reduced screening timelines	LSC-targeted drug identification; multi-target resistance-overcoming agents	Experimental validation (X-ray crystallography of confirmed hits)	[7]
Drug repurposing - AML	AML/LSC targets (polypharmacological)	FDA-approved compound structures; AML molecular targets	Structure-based virtual screening; molecular dynamics simulation	FDA-approved drug library	Identified repurposable compounds with polypharmacological AML/LSC activity	Rapid clinical translation; overcoming LSC therapy resistance via repurposing	In silico validation with molecular dynamics	[33]
E: Biomanufacturing and cell therapy production
HSC product quality control	CD34+ HSC products	Bright-field microscopy (manufacturing line)	Deep CNN (computer vision)	Clinical HSC manufacturing image dataset	High accuracy for cell viability and phenotype classification; validated vs flow cytometry	Real-time manufacturing QC; batch release decisions	Validated against the flow cytometry gold standard	[34]
CD34 yield prediction - cord blood	Cord blood HSCs (CD34+)	Pre/post-processing cell counts; CBU characteristics	Back-propagation ANN	802 CBUs	56.99% prediction accuracy for CD34 dose	Graft potency estimation; transplant planning	Validated on 802 CBUs	[35]
PBSC apheresis yield prediction	Autologous/allogeneic PBSC donors	Pre-apheresis CD34 counts; donor clinical characteristics	ML regression	Multi-site clinical apheresis dataset	Accurate harvest outcome prediction supporting collection scheduling	Collection efficiency; donor scheduling optimization	Clinical validation	[36]
Ex vivo HSC expansion optimization	HSCs in bioreactor culture	Multi-parameter sensors (O2, glucose, lactate, cytokine)	Reinforcement learning + digital twin modeling	Proof-of-concept bioreactor dataset	Optimized culture conditions; improved expansion yield via adaptive control	Expansion efficiency; GMP process optimization	Proof-of-concept validation	[37]
CAR-T manufacturing - smart hospital	CAR-T cell products (autologous)	Process sensor data; imaging; manufacturing records	AI + automation (Industry 4.0)	Pilot smart manufacturing hospital data	Reduced vein-to-vein time; standardized product quality	Scalable autologous CAR-T production; accessibility	Pilot implementation validation	[38]
Digital twin/Bioprocessing 4.0	Cell therapy products; HSC/CAR-T	Multi-parameter bioprocess sensor streams	Digital twin simulation; reinforcement learning; AI-assisted process control	Pharma manufacturing simulation datasets	Real-time process optimization; predictive quality control	GMP compliance; scalable biomanufacturing	Simulation and proof-of-concept	[8,9,39]
F: HSCT - transplantation, HLA matching, and GVHD
HLA genotyping extraction (NLP)	HSCT donors and recipients	Electronic health records (free text; 70000+ HLA reports)	Rule-based NLP (Python regex; 124 extraction + 8 cleaning rules)	70000+ HLA reports (Seoul National University Hospital)	Precision: 0.892-0.999; recall: 0795-0.998 across HLA-A, B, C, DR, DQ	Donor-recipient HLA matching; adverse drug reaction prediction	Clinical validation at the SNUH registry	[40]
Acute GVHD prediction (CNN-NLP)	HSCT recipients	Clinical notes + HLA typing data	CNN-NLP hybrid (word2vec encoding of HLA antigens/alleles)	18763 patients (Japanese Transplant Registry)	Stratified cumulative incidence 318% (low-risk) to 54.8% (high-risk); superior to Cox models	aGVHD risk stratification; immunosuppression planning	Registry-based validation (18763 patients)	[41]
Chronic GVHD phenotyping (ML + NLP)	HSCT patients with cGVHD	Clinical notes (organ involvement: Mouth, eye, liver, GI, joints, fascia, skin)	ML feature extraction + NLP clinical narrative analysis	Multicenter HSCT patient cohort	7 distinct cGVHD phenotypes; 2.24-fold mortality difference (high vs low risk); independent of NIH severity criteria	Mortality stratification; personalized immunosuppression	Multi-center clinical validation	[42]
Comprehensive HSCT AI care	HSCT recipients (full care pathway)	Transplant documentation; clinical narratives; registry data	NLP + ML (composite AI review)	Multi-institutional registry and EHR data	Automated complication parsing; infection management; NRM prediction	Early warning systems; donor selection; complication management	Multi-institutional review	[4]

AI: Artificial intelligence; HSC: Hematopoietic stem cell; MPP: Multipotent progenitor; scRNA-seq: Single-cell RNA sequencing; scATAC-seq: Single-cell assay of transposase-accessible chromatin sequencing; VIA: Voyager in single-cell RNA sequencing analysis; MCMC: Markov chain Monte Carlo; ADAPI: 4’,6-diamidino-2-phenylindole; CNN: Convolutional neural network; AUROC: Area under the receiver operating characteristic curve; ANN: Artificial neural network; BM: Bone marrow; LTHSC: Long-term hematopoietic stem cell; STHSC: Short-term hematopoietic stem cell; TPO: Thrombopoietin; SCF: Stem cell factor; ANGPT1: Angiopoietin-1; ROS: Reactive oxygen species; GRN: Gene regulatory network; SCENIC: Single-cell regulatory network inference and clustering; TF: Transcription factor; CITE-seq: Cellular indexing of transcriptomes and epitopes by sequencing; VAE: Variational autoencoder; PBMC: Peripheral blood stem cell; LSC: Leukemic stem cell; HSPC: Hematopoietic stem and progenitor cell; AML: Acute myeloid leukemia; ML: Machine learning; OS: Overall survival; ELN: European Leukemia Net; MRD: Measurable residual disease; XGBoost: Extreme Gradient Boosting; UMAP: Uniform manifold approximation and projection; AUC: Area under the curve; SHAP: SHapley Additive exPlanations; AML-CR: Acute myeloid leukemia complete remission; CR: Complete remission; DL: Deep learning; CLL-1: C-type lectin-like molecule-1; CAR-T: Chimeric antigen receptor T-cell; XAI: Explainable artificial intelligence; MMSC: Multiple myeloma stem cell; MM: Multiple myeloma; MGUS: Monoclonal gammopathy of undetermined significance; NDMM: Newly diagnosed multiple myeloma; FDA: Food and Drug Administration; QC: Quality control; CBU: Cord blood unit; GMP: Good Manufacturing Practice; HSCT: Hematopoietic stem cell transplantation; GVHD: Graft-vs-host disease; NLP: Natural language processing; HLA: Human leukocyte antigen; aGVHD: Acute graft-vs-host disease; cGVHD: Chronic graft-vs-host disease; NRM: Non-relapse mortality.