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

Table 2 Public datasets and computational tools specifically applicable to hematopoietic stem-cell artificial intelligence research

Stem-cell anchor	Resource name	Resource type	What it’s for (HSC/LSC-specific)	Access/Ref.
A: Reference atlases for HSC → lineage differentiation (where “stemness” lives)
Human bone-marrow baseline map for projecting new normal/AML datasets and interpreting “where the cell sits” on the HSC → lineage hierarchy	BoneMarrowMap	scRNA-seq atlas + projection tool	Reference for CD34+ HSPC balance + mature compartments; projection/classification of new hematopoietic or leukemic cells	GitHub/[90]
Canonical mouse HSPC differentiation landscape used as benchmark for TI and lineage priming	Nestorowa et al[91] Mouse HSPC atlas	scRNA-seq	Mouse HSPC heterogeneity + lineage trajectories; common benchmark dataset	GEO GSE81682/[91]
Early-life/developmental hematopoiesis context	Perinatal bone marrow development atlas	scRNA-seq	Niche development + marrow colonization programs	[92,93]
Cord blood HSC heterogeneity (neonatal stemness programs; ex vivo culture/transplant relevance)	Human cord blood HSC populations	scRNA-seq	CD34pos vs CD34neg HSC populations; neonatal HSC state differences	GEO GSE237832/[94]
Human HSC activation states (quiescence → activation continuum)	Human HSC activation trajectory	scRNA-seq (SMART-seq2)	Activation trajectories in the primitive CD34+CD38-CD45RA- compartment	EGA study1
Integrated RNA + chromatin programs across hematopoiesis (epigenetic stemness + TF logic)	Murine HSPC multimodal atlas	scRNA-seq + scATAC-seq	Joint transcriptomic/epigenetic differentiation programs	[95]
B: Multimodal phenotype ↔ transcriptome (HSC/LSC immunophenotype as a first-class signal)
Method benchmark for RNA + protein integration (not HSC-specific, but enables HSC surface-marker integration)	PBMC CITE-seq (10X genomics)	CITE-seq (RNA + protein)	Benchmarking multimodal integration models, you then apply them to marrow HSPC/LSC	10X datasets referenced as common benchmarks in the totalVI context[15]
High-parameter immune niche cell reference (HSC niche signals/immune interactions)	Murine spleen/lymph node CITE-seq	CITE-seq	Large protein panel; good demonstration dataset for multimodal modeling	[15]
Core computational framework for CITE-seq (HSC/LSC immunophenotyping)	totalVI	Multi-omics integration	Joint latent space (RNA + protein), batch correction, missing-protein handling → directly supports HSC/LSC surface phenotype + transcriptional state integration	[15]
C: Mechanistic “stemness regulators”: GRNs, TF programs, causality-leaning inference
In silico TF perturbation to test “does this TF maintain HSC identity or drive differentiation?”	CellOracle	GRN inference + perturbation simulation	Demonstrated in mouse and human hematopoiesis; supports mechanistic framing in a stem-cell review	[14] and GitHub[96]
TF-activity inference from scRNA-seq to link stage-specific regulators with differentiation states	SCENIC	TF network inference	Regulatory network + regulon activity per cell; can be used to interpret HSC/LSC programs	[13]
D: TI (to resolve rare HSC states, branching, cycling)
Capturing complex hematopoietic branching while preserving rare populations (useful for “rare HSC/pre-LSC” arguments)	VIA	TI	Lazy-teleporting random walks + MCMC; marketed as scalable/generalized TI	[5]
E: Handling missing modalities + batch effects in HSC multi omics (practical translational reality)
Multi-omic integration when HSC datasets are incomplete/unpaired across assays	scMaui	Multi-omics integration	Product-of-experts VAE; batch + missing modality handling	[16]
F: Aging/activation phenotypes in HSCs (imaging + AI)
Imaging-based “biological age” predictor from HSC nuclear architecture	ChromAgeNet	Chromatin aging prediction	CNN on 3D DAPI chromatin images; positions aging as a learnable HSC phenotype	[6]
G: LSC persistence proxies in patients: MRD/residual disease
Interpretable ML that supports MRD assessment (detecting residual immature/LSC-like compartments)	MAGIC-DR	MRD detection	Interpretable ML-guided approach for AML MRD	PubMed landing[56]
Notable for making a large AML flow cytometry dataset publicly available for benchmarking computational MRD tools	Computational MRD assessment (GMM + novelty detection)	MRD detection	Standardization + automated MRD explicitly discusses heterogeneity issues that connect to LSC persistence framing	[97]

¹Dataset: Cord blood hematopoietic stem cell single-cell RNA sequencing data (CD34+ vs CD34- populations) publicly available at NCBI GEO:GSE237832 (hyperlink: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE237832).

HSC: Hematopoietic stem cell; LSC: Leukemic stem cell; AML: Acute myeloid leukemia; scRNA-seq: Single-cell RNA sequencing; HSPC: Hematopoietic stem and progenitor cell; TI: Trajectory inference; GEO: Gene Expression Omnibus; EGA: European Genome-phenome Archive; TF: Transcription factor; scATAC-seq: Single-cell assay for transposase-accessible chromatin sequencing; PBMC: Peripheral blood mononuclear cell; CITE-seq: Cellular indexing of transcriptomes and epitopes by sequencing; GRN: Gene regulatory network; SCENIC: Single-cell regulatory network inference and clustering; VIA: Voyager in single-cell RNA sequencing analysis; MCMC: Markov chain Monte Carlo; VAE: Variational autoencoder; AI: Artificial intelligence; CNN: Convolutional neural network; DAPI: 4’,6-diamidino-2-phenylindole; MRD: Measurable residual disease; MAGIC-DR: Machine-learning guided approach to acute myeloid leukemia measurable residual disease detection in residual disease; ML: Machine learning; GMM: Gaussian mixture model.