A human developmental and adult brain atlas benchmarks dopaminergic stem cell models and cell therapy candidates
Parkinsons disease (PD) is characterized by the progressive loss of midbrain dopaminergic (mDA) neurons1. Stem cell-derived mDA neurons hold promise for disease modelling2,3 and are currently in clinical trials for cell replacement therapy4-6. However, systematic benchmarking has been limited by the lack of a unified high-resolution reference and methods that quantify incomplete or mixed lineage specification in vitro7. We establish a single-cell and spatial atlas of the human developing diencephalon-midbrain-hindbrain axis resolving 93 cell subtypes, including 39 lacking prior single-cell characterization and 4 entirely novel populations. Using this atlas as a reference, we integrate 19 hPSC-derived mDA datasets, both published2,8-25 and unpublished, to build the Human Dopaminergic Neural Atlas (HDNA) spanning 2D, 3D, and graft models, including those used in clinical trials. To classify cells and quantify lineage fidelity, we develop CapybaraBrain, a marker-driven non-negative decomposition framework that assigns each cell continuous identity scores across all 93 developmental programs, enabling systematic discrimination of discrete, transitioning, and cross-lineage hybrid states26. We uncover a pervasive landscape of off-target populations reflecting relaxed transcriptional boundaries in vitro, including a previously unrecognized TH-PITX2 midbrain neuronal population, and we validate atlas-predicted latent lineage plasticity through inducible genetic fate mapping in mouse models. We further define maturation-associated transcriptional programs by harmonizing adult mDA subtype atlases, revealing that dopaminergic identity and maturation are partially decoupled across protocols. Finally, projecting PD patient-derived tri-cultures onto the HDNA uncovers genotype- and cell-type-specific transcriptional dysregulation. Together, these integrated atlases and computational framework establish a unified standard for benchmarking differentiation fidelity, exposing off-target states, and guiding next-generation PD models and cell therapies.