The central data resource is same-cell paired multi-omics. Technologies including sci-CAR, SNARE-seq, SHARE-seq, Paired-seq and 10x Multiome measure gene expression and chromatin accessibility in the same cell, allowing candidate regulatory elements to be connected to transcriptional output. Their development changed the inference problem: accessibility indicates regulatory potential, whereas expression reflects the current result of regulation, and the relationship is neither instantaneous nor deterministic. A promoter may become accessible before its gene is induced, while an occupied enhancer may not alter steady-state expression. Matched-but-unpaired measurements offer a less direct alternative, and deep generative systems such as scMVP and scMOG can impute one modality from the other. These strategies expand usable data, but they introduce anchoring assumptions and model-dependent artifacts that must be tested rather than treated as equivalent to simultaneous observation.

Among interpretable approaches, motif and co-accessibility methods remain important. SCENIC combines co-expression modules with transcription-factor motif enrichment to score regulons, while SCENIC+ extends this logic to paired multi-omics by proposing enhancer-driven networks linking regulatory elements, transcription factors and target genes. Other systems infer links through different definitions of regulatory potential. scREG combines expression and accessibility to construct subpopulation-specific cis-regulatory networks, while FigR identifies domains of regulatory chromatin and links them to genes in stimulated immune cells. DIRECT-NET uses gradient boosting to nominate cis-regulatory elements and their targets, including within subpopulations or dynamic states. MICA adds mutual information and accessibility refinement for settings with few cells, capturing non-monotonic dependencies that simple linear correlation can miss. These methods offer biological interpretability, but their dependence on sequence motifs, local co-variation and selected priors limits their ability to represent nonlinear or global regulation.

Deep integration and graph learning are designed for the scale, sparsity and complexity of single-cell multi-omics. LINGER combines paired-cell data with atlas-scale external expression information and transcription-factor motif priors through manifold regularization, then transfers inferred networks to expression-only samples to estimate transcription-factor activity. scMultiomeGRN uses neighbor aggregation and cross-modal attention to construct cell-specific networks. DeepMAPS represents genes, peaks and cells as a heterogeneous graph and applies a graph transformer to learn local and global relationships. GLUE performs graph-linked unified embedding for multi-omics integration at scales reaching millions of cells. Other approaches add directed priors, global autoencoders or spatial associations to graph attention. These architectures can exploit topology beyond local correlation, but their results depend heavily on graph construction, prior quality, annotations and benchmark labels, meaning a larger model is not automatically a more reliable regulatory model.

Because tumor states can be continuous and plastic, several methods explicitly model dynamics. Dictys combines multi-omics footprinting, stochastic process networks and probabilistic models to infer context-dependent networks along developmental continua, while inferCSN uses pseudotime and accounts for uneven cell distributions when estimating regulatory relationships. scTIGER uses attention-based convolutional networks to detect condition-specific rewiring, and scMTNI applies multi-task learning to lineages and cell types. A separate family tests candidate causal hypotheses through virtual perturbation. CellOracle removes or alters inferred transcription-factor relationships to simulate loss of function; Pando combines multi-omics, binding-site predictions and organoid experiments; and GenKI and scTenifoldKnk generate virtual knockouts from wild-type data. These simulations can prioritize regulators, but a predicted knockout response remains a hypothesis until reproduced by CRISPR knockout, CRISPR interference, Perturb-seq or another orthogonal experiment.

Benchmarking exposes a gap between network reconstruction and biological usefulness. Common edge metrics, including area under the receiver-operating-characteristic curve, area under the precision-recall curve, early precision, odds ratio and statistical confidence, are affected by class imbalance, incomplete labels, threshold choices and the limited coverage of reference networks. Resources such as ENCODE and ChIP-seq provide valuable binding evidence but remain incomplete and biased toward particular cell types, while DREAM and BEELINE benchmarks were built largely for earlier inference settings. Perturb-seq and single-cell CRISPR screens add functional measurements but cover restricted genes, states and contexts. The review therefore recommends donor- or cell-type-aware test splits, reporting of label coverage and observable metric ceilings, tests against simple co-expression and gene-level baselines, negative controls, subsampling checks, hyperparameter sensitivity analysis and code availability.

Cancer applications show why cell-state specificity may matter. In a mouse model of skin squamous-cell carcinoma, paired single-cell RNA and chromatin-accessibility profiles separated epithelial-mesenchymal-transition states, while CRISPR/Cas9 loss-of-function experiments supported Klf5 and Pitx1 as regulators of early EMT and metastasis and Nfatc1 and Creb3l1 as later-stage regulators. In melanoma, quantitative state diagrams derived from regulatory networks identified several resistance routes, including dedifferentiation and a hyperpigmented state, and proposed combination strategies that reduced predicted occupancy by resistant states. The latter remained primarily computational, whereas the squamous-cell-carcinoma findings included functional perturbation. This contrast illustrates why a network can be useful for generating mechanistic hypotheses even when its proposed interventions have not yet been tested experimentally.

Resistance, lineage plasticity and tumor classification provide further applications. In patient-derived glioma stem-like cells, inferred network topology distinguished trajectories through resistant and sensitive intermediate states under drug treatment, and simulations guided siRNA or combination approaches that increased killing in the preclinical system. A TCF4-dependent network in melanoma was linked to immunotherapy resistance and supported by functional perturbation. Single-cell and bulk integration distinguished two epithelial tumor states in colorectal cancer, while approaches in hepatocellular carcinoma identified immune-activated, metabolic and proliferative terminal states; the proliferative signature was associated with worse overall survival but had not been prospectively validated. Other studies resolved age-related regulatory signatures in acute myeloid leukemia, progression-associated epigenetic programs in lung adenocarcinoma, a BAP1-associated interferon program in clear-cell renal cell carcinoma, FOXA1-driven lineage transformation in prostate cancer and carcinogenesis-associated plasticity in gastric-cell isthmus states.

Networks are also being extended beyond malignant cells. Multi-omics in triple-negative breast cancer identified a myofibro-inflammatory program in cancer-associated fibroblasts, indicating that stromal states are maintained by coordinated regulation. Future spatial and multi-omics methods will need to separate cell-intrinsic networks from ligand-receptor effects and incorporate cell neighborhoods and physical proximity. Larger ambitions include adding DNA methylation, histone modifications, spatial information and temporal measurements, combining foundation models with virtual perturbation and CRISPR screens, and building benchmarks across tumor types and platforms. Foundation models such as GET, EpiAgent, RegFormer and ChromBERT may improve transfer across sparse cell states, but attention-derived edges can add little beyond gene-level statistics, and internal feature relations do not establish biological causation. The review is a structured expert synthesis rather than a systematic review or meta-analysis, and the field’s decisive next step is not simply more elaborate architectures but shared benchmarks, uncertainty estimates, transparent reporting and causal validation in organoids, primary samples and clinically annotated cohorts.

Subject of Research: AI-assisted reconstruction and validation of cell-state-specific gene regulatory networks in cancer

Article Title: AI and computational methods for reconstructing cell-state-specific gene regulatory networks from single-cell transcriptome and chromatin accessibility data in cancer: methods, applications, and perspectives

Article References: Wang, J., & Chen, F. (2026). AI and computational methods for reconstructing cell-state-specific gene regulatory networks from single-cell transcriptome and chromatin accessibility data in cancer: methods, applications, and perspectives. Frontiers in Genetics, 17, Article 1982712. https://doi.org/10.3389/fgene.2026.1982712

Image Credits: AI Generated

DOI: 10.3389/fgene.2026.1982712

Keywords: single-cell multi-omics, gene regulatory networks, cancer cell states, artificial intelligence, chromatin accessibility, transcriptomics, causal validation, drug resistance, tumor heterogeneity, virtual perturbation, computational, methods