Ning Wang

Integrating multimodal data is essential to advance neuroscience as a discipline. Large-scale collaborative efforts, exemplified by the BRAIN Initiative, together with advances in single-cell and spatial omics, multimodal imaging, human genetics, behavioral phenotyping, protein structure prediction, and related technologies, are transforming neuroscience into a data-rich, highly interconnected science. As experimental resources, scientific literature, and collaborative networks continue to expand, integrating discoveries across modalities, species, and levels of biological organization has become the field's central bottleneck. This Perspective argues that artificial intelligence (AI) is becoming essential for overcoming these constraints. Using six evolving approaches we outline strategies to accelerate AI adoption through community building and pilot projects that drive AI transformation at both the organizational and technological levels. Organizational transformation cultivates broad AI awareness and a pro-AI culture while technological transformation improves the efficiency, scalability, and quality of data analysis, allowing researchers to generate insights more rapidly and effectively. Together, these advances position AI as a key driver for advancing neuroscience at scale.

SUMMARY: Single-cell RNA technologies enable the routine acquisition of transcriptomic atlases. However, these molecular profiles are influenced by overlapping sources of variation. Since these covariates confound comparisons, data integration is the first step in most analyses. Three challenges remain: correcting strong batch effects, scaling to millions of cells, and modeling how covariates influence gene expression. To address these challenges, we developed PIANO: Probabilistic Inference Autoencoder Networks for multi-Omics, a deep learning framework whose central feature is a generative model of gene expression data. Additionally, PIANO achieves robust integrations and trains 10x faster than previous methods. PIANO accurately integrates single-cell data across species and across single-cell and spatial transcriptomics modalities. As practical applications, PIANO models spatially-resolved gene expression during Alzheimer’s disease progression in human brains and integrates over 100 million cancer cells to model drug perturbations. In summary, PIANO’s integration and generative modeling capabilities will empower novel insights for countless future studies.

Mammalian brain cell identity is shaped by intrinsic factors and external context. We present a spatially resolved transcriptomic and gene regulatory atlas of cell types found across all subcortical brain regions in a primate - the marmoset monkey. Dense sampling and cross-species integrations revealed spatially precise neuronal assemblies, including in complex structures such as hypothalamus. We find chromatin accessibility and transcriptional identity are spatially tuned within and across subcortical structures. Spatial gradients within subfields of the hippocampal formation are predominantly orchestrated by graded transcription factors coupled with graded enhancers. The primate-expanded population of GABAergic neurons in the thalamus shares transcriptional and regulatory syntax with neurons in superior colliculus, reflecting an evolutionary adaptation compared with rodents. We show that unexpected transcriptional convergence, such as between striatal GABAergic medium spiny neurons and telencephalic glutamatergic neurons, can arise when distinct gene regulatory networks impinge on the same downstream genes.

SUMMARY: Advances in single-cell genomics technologies enable the routine acquisition of atlases with millions of cells. These datasets often include multiple covariates, such as donors, sequencing platforms, developmental timepoints, and species, which provide new opportunities for discovery and new challenges. To mitigate unwanted sources of variation, dataset integration is the starting point for most analyses. However, existing methods struggle with integrating large complex datasets. To address these limitations, we developed PIANO, a variational autoencoder framework that uses a negative binomial generalized linear model for stronger batch correction, and code compilation for ten times faster training than existing tools. We first demonstrate performant integration compared to commonly used methods on single-species datasets. We then show PIANO enables superior analyses of multiple atlases, solving challenging integration tasks across sequencing platforms, development, and species, while simultaneously preserving desired biological signals. Our contributions include a novel, high-performance integration method and recommendations for integration applications.

The brain controls nearly all bodily functions via spinal projecting neurons (SPNs) that carry command signals from the brain to the spinal cord. However, a comprehensive molecular characterization of brain-wide SPNs is still lacking. Here we transcriptionally profiled a total of 65,002 SPNs, identified 76 region-specific SPN types, and mapped these types into a companion atlas of the whole mouse brain¹. This taxonomy reveals a three-component organization of SPNs: (1) molecularly homogeneous excitatory SPNs from the cortex, red nucleus and cerebellum with somatotopic spinal terminations suitable for point-to-point communication; (2) heterogeneous populations in the reticular formation with broad spinal termination patterns, suitable for relaying commands related to the activities of the entire spinal cord; and (3) modulatory neurons expressing slow-acting neurotransmitters and/or neuropeptides in the hypothalamus, midbrain and reticular formation for ‘gain setting’ of brain–spinal signals. In addition, this atlas revealed a LIM homeobox transcription factor code that parcellates the reticulospinal neurons into five molecularly distinct and spatially segregated populations. Finally, we found transcriptional signatures of a subset of SPNs with large soma size and correlated these with fast-firing electrophysiological properties. Together, this study establishes a comprehensive taxonomy of brain-wide SPNs and provides insight into the functional organization of SPNs in mediating brain control of bodily functions.