Chuangyi Han

Spatial omics technologies provide unprecedented opportunities to investigate how molecular programs are organized within tissues. However, existing analytical frameworks primarily focus on identifying cellular states and characterizing differentially expressed molecular features across these states. The relational spatial organization of molecular features and its dynamics across biological contexts therefore remain largely unexplored. Here we present Gene SPatial Optimal Transport (GeneSPOT), a gene-centric computational framework for characterizing the spatial relationships among genes and quantifying their reorganization across biological conditions. Although gene-centric in design, GeneSPOT generalizes to other molecular features. Across diverse applications, GeneSPOT resolved tissue structures, integrated paired and unpaired spatial multi-omics data, unveiled the spatial convergence of gene programs during embryonic development and identified aging-associated spatial reorganization among genes whose expression did not change significantly with age. GeneSPOT thus complements conventional cell-centric analysis, providing a framework for deciphering how spatial relationships among molecular features are reorganized across biological conditions and over time.

While spatial multiomics offers insights into complex biological systems, its widespread adoption is hindered by technical challenges, specialized requirements and limited accessibility. Here we present NicheTrans, a spatially aware cross-omics translation method and a flexible Transformer-based multimodal framework. Unlike existing single-cell translation methods, NicheTrans incorporates both cellular microenvironment information and multimodal data. We validated the advantage of NicheTrans across diverse biological cases. Through NicheTrans, we uncovered spatial multiomics domains that were not detectable through single-omics analysis alone. Model interpretation revealed key molecular relationships, including gene programs associated with dopamine metabolism and amyloid β-associated cell states. In addition, using translated protein markers as spatial landmarks, we quantified the spatial organization of key glial cell subtypes in the Alzheimer’s disease brain. NicheTrans represents a powerful tool for generating comprehensive spatial multiomics insights from more accessible single-omics measurements, making multiomics analysis more feasible for the broader research community.

Spatial omics technologies enable the precise detection of proteins and RNAs at high spatial resolution. Designing spatial omics experiments requires careful consideration of "what" targets to measure and "where" to position the field of views (FOVs). Current FOV sampling strategies often involve acquiring densely sampled FOVs and stitching them together, which is time-consuming, resource-intensive, and sometimes impossible. To optimize FOV sampling strategies, we propose SOFisher, a reinforcement learning-based framework that harnesses the knowledge gained from the sequence of previously sampled FOVs to guide the selection of the next FOV position, to improve the efficiency of capturing more regions of interest. We rigorously evaluated SOFisher's performance using comprehensive simulations based on real spatial datasets, and our results clearly demonstrated that SOFisher consistently outperformed the conventional approach across various metrics. SOFisher's robustness and generalizability were further validated through cross-domain generalization tests and its adaptability to varying FOV sizes. On a real Alzheimer's Disease (AD) dataset, SOFisher successfully guided the selection of FOVs containing neurofibrillary tangles and amyloid-β plaques in both single and dual target tissue landmark scenarios. Remarkably, with the trained SOFisher policy, the guided experiment design of spatial single-omics on small number of FOVs yielded insights into AD-related cell states, subtypes, and gene programs previously obtained through spatial multi-omics experiments on large tissue slices. We further showcased SOFisher's applications on a colorectal cancer dataset with complex tissue structures and high heterogeneity. Beyond cell type based targeting, we extended SOFisher's reward function to maximize gene expression levels across diverse spatial patterns and enhanced its exploration capacity through SOFisherWR (SOFisher With Restart) to comprehensively capture discontinuous target enriched regions. SOFisher has the potential to revolutionize the experiment design of spatial biology.

Reconstructing the three-dimensional molecular architecture of tissues from two-dimensional spatial transcriptomics slices is a central goal in spatial biology. Spatial alignment, the computational registration of multiple tissue slices using their spatial coordinates and gene expression profiles, provides the foundational framework for this integrative perspective. Although numerous alignment methods have emerged, a comprehensive benchmark to guide their application has been notably absent. Here we address this by systematically evaluating a diverse suite of leading methods. Executing 295 distinct alignment tasks across diverse datasets and technologies, our framework quantifies method accuracy, efficiency, usability and robustness, while also assessing the downstream impact of alignment quality. Crucially, our study systematically investigates performance in challenging real-world scenarios, uncovering substantial limitations in current tools. To address these bottlenecks, we propose and validate effective mitigation strategies. Finally, we provide practical guidelines to assist researchers in selecting the optimal alignment method and optimizing their analytical workflows.

Spatial transcriptomics (ST) has revolutionized our understanding of tissue architecture, yet constructing comprehensive three-dimensional (3D) cell atlases remains challenging due to technical limitations and high cost. Current approaches typically capture only sparsely sampled two-dimensional sections, leaving substantial gaps that limit our understanding of continuous organ organization. Here, we present SpatialZ, a computational framework that bridges these gaps by generating virtual slices between experimentally measured sections, enabling the construction of dense 3D cell atlases from planar ST data. SpatialZ is designed to operate at single-cell resolution and function independently of gene coverage limitations inherent to specific spatial technologies. Comprehensive validation demonstrates that SpatialZ accurately preserves cell identities, gene expression patterns and spatial relationships. Leveraging the BRAIN Initiative Cell Census Network data, we constructed a 3D hemisphere atlas comprising over 38 million cells. This dense atlas enables new capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, 3D mapping of query tissue sections, and discovery of 3D spatial molecular architectures through new synthesized views. To demonstrate its extensibility beyond transcriptomics, we applied SpatialZ to imaging mass cytometry data from human breast cancer, successfully deciphering 3D spatial gradients within the tumor microenvironment. Our approach generates cell atlases that provide previously unattainable 3D resolution of spatial molecular landscapes.