The core insight behind TFdisc is that wild-type single-cell transcriptomes already encode a surprising amount of information about how cells would respond if a regulatory gene were lost. The simulator works by first reconstructing gene regulatory networks from reference single-cell RNA sequencing data, mapping which transcription factors are likely to control which target genes based on co-expression patterns, regulatory motifs and expression dynamics across cell states. When a user specifies a transcription factor to perturb, TFdisc propagates the simulated disruption through this network, adjusting the expression of downstream targets and then recomputing the resulting cell states. The output is a predicted post-perturbation single-cell dataset: a virtual census of how each cell type would respond, which genes would shift their expression, and whether cells would drift toward or away from their normal identities and differentiation trajectories.

Validation was a central concern for the team, because a simulator is only useful if its predictions match reality. The researchers benchmarked TFdisc against multiple experimental perturbation datasets in which transcription factors had genuinely been knocked out or knocked down in the laboratory. Across these datasets, the model proved accurate in three demanding tasks: reconstructing the underlying gene regulatory networks, identifying the differentially expressed genes that change after perturbation, and predicting shifts in cell identity and lineage differentiation. The authors also compared TFdisc against existing perturbation-prediction tools, including approaches that require experimental knockout data for training, and found that their simulator, which can operate on wild-type data alone, performed competitively. Supplementary analyses covered imputation methods for sparse single-cell data, robustness of network construction, and sensitivity analyses designed to confirm that the findings were not artifacts of normalization or dataset size.

With the simulator validated, the team turned it toward one of the most stubborn problems in neuroscience: the genetic architecture of brain disorders. Conditions such as schizophrenia and autism spectrum disorder are influenced by large numbers of genetic risk variants, most of which individually contribute only a small amount of risk. Genome-wide association studies and sequencing efforts have catalogued hundreds of candidate risk genes, but converting those lists into mechanistic understanding has proven extraordinarily difficult. TFdisc offered a way to ask a question that would otherwise require decades of animal work: what happens to the developing and adult human brain, cell by cell, when each of these risk transcription factors is perturbed, alone or in combination?

The simulations revealed that different brain disorders are built on strikingly different genetic blueprints. When the researchers simulated the simultaneous perturbation of multiple schizophrenia risk transcription factors, the individual effects appeared scattered and heterogeneous, yet when combined they converged on a coherent set of shared molecular pathways. The team described this architecture as a jigsaw mechanism: each risk factor contributes one piece, and the disorder emerges only when many pieces are assembled together, with no single gene sufficient to produce the disease-relevant disruption. Autism spectrum disorder showed the opposite pattern. Perturbing its risk transcription factors produced overlapping, redundant effects in which individual factors each engaged a common core of pathways, an architecture the authors termed a monolithic mechanism. In this picture, many different genetic insults funnel into a similar biological outcome, which may help explain why autism can arise from such a wide variety of genetic lesions yet present as a recognizable clinical syndrome.

The simulator also traced how disease-relevant perturbation effects unfold across development. By applying TFdisc to single-cell reference data spanning prenatal development through adulthood, the researchers could identify the cell types and developmental stages in which simulated perturbations of risk factors produced their strongest molecular signatures. The supplementary materials document cell-type-stage-specific effects from prenatal development to adulthood, and independent validation of the predicted developmental regulatory programs using an external dataset confirmed that the simulator’s developmental predictions held up against real data. Shared pathways between schizophrenia and autism were detectable across developmental stages, but the timing and cellular context of peak pathway activity differed between the two disorders, reinforcing the conclusion that they follow distinct trajectories despite overlapping genetic risk.

Technically, the pipeline behind these findings involved several layers of quality control. The team benchmarked four imputation methods for handling the dropout and sparsity that plague single-cell RNA sequencing, evaluated gene regulatory network construction through robustness and sensitivity analyses, and assessed performance on simulated single-cell datasets where ground truth was known. Curated lists of transcription factors and risk genes associated with ten diseases were assembled from public databases and the literature, and pathway enrichment results were de-redundant to avoid inflating apparent biological signal with overlapping gene ontology terms. Clustering analyses of the risk transcription factors themselves helped organize the jigsaw and monolithic patterns, and phenotypic analyses connected the simulated perturbation clusters to known disease characteristics. This methodological scaffolding is important because perturbation prediction is a young field in which evaluation standards are still being established, and the Fudan team’s benchmarks provide a template for how future simulators should be assessed.

The implications for drug discovery and experimental design are considerable. A validated in silico perturbation simulator allows researchers to prioritize which transcription factors, cell types and developmental windows deserve the most intensive laboratory attention, dramatically narrowing a search space that would otherwise be prohibitive. For schizophrenia, the jigsaw architecture suggests that therapeutic strategies aimed at a single risk gene may be doomed to fail, and that interventions targeting the convergent downstream pathways, or combinations of factors, may be more promising. For autism, the monolithic architecture implies that a therapy correcting the shared core pathways could potentially benefit patients whose conditions arise from very different genetic causes. More broadly, the approach demonstrates that computational simulation can serve as a first-pass screen for perturbation biology, generating testable hypotheses about gene function at a scale unattainable in the wet laboratory.

The work, supported by funding from China’s National Key Research and Development Project, the National Science and Technology Innovation 2030 Major Program, the National Natural Science Foundation of China and the Shanghai Science and Technology Commission, arrives as single-cell biology and artificial intelligence converge on the problem of complex disease. The authors caution that their simulations are predictions, not proof, and that experimental validation of specific perturbation effects remains essential. Yet the study offers a compelling demonstration that the regulatory code written into ordinary single-cell data can be decoded to reveal how genetic risk is transformed into molecular dysfunction. As perturbation datasets accumulate and simulators like TFdisc are refined, the prospect of systematically mapping the causal paths from risk variant to altered cell state, and ultimately to disorder, moves from aspiration toward routine practice, promising a more mechanistic era for psychiatric genetics.

Subject of Research: An in silico transcription factor perturbation simulator that models gene regulatory responses in brain disorders using single-cell RNA sequencing data.

Article Title: An in silico transcription factor perturbation simulator uncovers diverse genetic architectures of brain disorders

Article References: Wang, H., Li, Q., & Zhu, Y. (2026). An in silico transcription factor perturbation simulator uncovers diverse genetic architectures of brain disorders. Genome Medicine. https://doi.org/10.1186/s13073-026-01752-5

Image Credits: AI Generated

DOI: 10.1186/s13073-026-01752-5

Keywords: transcription factors, single-cell RNA sequencing, gene regulatory networks, schizophrenia, autism spectrum disorder, brain disorders, in silico perturbation, TFdisc, genetic architecture, computational biology, Genome Medicine, Fudan University