Shu Dan

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.

The basal ganglia (BG) are conserved brain regions essential for motor control, learning, emotion, and cognition, and are implicated in neurological and psychiatric disease. Yet a unified cross-species taxonomy of BG cell types is lacking, limiting translation of BG circuit mechanisms, interpretation of human genetic risk, and development of cell type-targeted tools. We present a multiomic consensus atlas of 1.8 million nuclei from human, macaque, and marmoset spanning eight BG structures. Integrating cross-species gene expression, open chromatin, and spatial profiling enables definition of conserved and divergent cell types. Alignment to existing mouse and human atlases identifies 61 homologous cell types conserved over 80 million years. We identify a STRd D2 StrioMat Hybrid medium spiny neuron (MSN) type with molecular, electrophysiological, and morphological features that clarify hybrid MSN identities. Comparative cis-regulatory analysis reveals conserved sequence grammars that encode cell identity and inform viral targeting strategies, providing a foundational resource for BG evolution, function, and disease.

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.

The basal ganglia are interconnected subcortical nuclei with complex topographical organization that orchestrate goal-directed behaviors and are implicated in neurodegenerative movement disorders. We generated a cellular-resolution, spatial transcriptomic atlas of the basal ganglia in human, rhesus macaque, and common marmoset, sampling over one million cells in each species. By integrating spatial data with a cross-species, consensus snRNA-seq cell type taxonomy, this atlas reveals conserved principles of molecular organization within and across structures. The cellular architecture is complex but highly stereotyped, with gene expression gradients superimposed onto discrete compartments. Extensive spatial sampling illuminates 3D gradients of molecular organization in the striatum and reveals cell type-specific core and shell compartments in the primate internal globus pallidus, which is conserved with mouse. This unified, cross-species spatial transcriptomic atlas will be a foundational resource for characterizing the molecular and functional organization of the basal ganglia and their roles in health and disease.

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 form of an animal’s limbs has to balance multiple functions: locomotion, grasping, climbing, and jumping, among others. For cryptic animals, especially those that resemble elements of their habitat like sticks or grasses, the limbs may also be modified to enhance the camouflage. The performance of a limb in one category may require a tradeoff, reducing performance in another category. Praying mantises provide a diverse group of insects who all use their forelegs for one function, capturing prey, while some species use them as part of their camouflage. Here we use a large database of images of mantis species to capture the variation in morphology across the order, and to calculate the largest prey that their forelegs can hold. We find that the length and thickness of the femur and the length of the tibia comprise most of the variability across species. The majority of species have similar foreleg morphology, with two large groups extending into areas of the morphospace with thicker or thinner forelegs. A geometric relationship between dimensions of the foreleg and the optimal prey diameter maps directly onto the variability across species determined by principal components analysis; legs with thinner femurs and shorter tibia can’t hold large prey, and the distribution of the species across the morphospace follows the gradient of optimum prey size. These results suggest that some species trade ability to grasp larger prey for benefits including crypsis, and the praying mantises are an ideal system for studying morphological and functional variation in limbs.

In this paper, we propose a method of generating a sketch map drawing and its instructions in order to support the route understanding of a human. Both are given from a structured data, called the road network, which is a graph augmented by the attributes of roads and crossings. In generating a sketch map drawing, the pictorial information about the shape and the direction of roads is modified. The sketch map drawing can be generated at any simplification level by controlling a parameter called simpleness. The instructions corresponding to the sketch map are produced by assigning appropriate terms into sentence templates. We verified that favorable results are obtained by this method.