Biology subjects

CUI, Y.

Publications and source records attributed to CUI, Y..

3 recordsLinked to original sources

bioRxiv2024

Bridging the Dimensional Gap from Planar Spatial Transcriptomics to 3D Cell Atlases

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 using real 3D ST and independent serial sectioning datasets demonstrates that SpatialZ accurately reconstructs virtual slices while preserving 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, a scale not feasible experimentally. This dense atlas enables unprecedented capabilities, including in silico sectioning at arbitrary angles, explorations of gene expression across both 3D volumes and surfaces, and 3D mapping of query tissue sections. While currently validated for spatial transcriptomics, the underlying principles of SpatialZ could potentially be adapted for spatial proteomics, spatial metabolomics, and even spatial multi-omics. Validated through internal and external testing, our computationally generated atlas maintains biological accuracy, providing unprecedented resolution of spatial molecular landscapes and demonstrating the potential of computational approaches in advancing 3D ST.

bioinformatics↗

bioRxiv2024

NicheTrans: Spatial-aware Cross-omics Translation

Spatial omics technologies have revolutionized our studies on tissue architecture and cellular interactions at single-cell resolution. While spatial multi-omics approaches offer unprecedented insights into complex biological systems, their widespread adoption is hindered by technical challenges, specialized requirements, and limited accessibility. To address these limitations, we present NicheTrans, the first spatially-aware cross-omics translation method and a flexible Transformer-based multi-modal deep learning framework. Unlike existing single-cell (non-spatial) translation methods, NicheTrans uniquely incorporates both cellular microenvironment information and flexible integration of multi-modal data, such as morphology and prior knowledge. We validated NicheTrans across diverse biological cases: Parkinsons Disease (PD), Alzheimers Disease (AD), breast cancer, and lymph nodes. Our approach demonstrated superior performance compared to existing single-cell methods, highlighting the crucial role of spatial and multi-modal information in cross-omics translation. Through NicheTrans, we uncovered spatial multi-omics 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 {beta}-associated cell states. Additionally, using translated protein markers as spatial landmarks, we quantified the spatial organization of key glial cell subtypes in the AD brain. NicheTrans represents a powerful tool for generating comprehensive spatial multi-omics insights from more accessible single-omics measurements, making multi-omics analysis more feasible for the broader research community.

bioinformatics↗

bioRxiv2024

MARVEL: Microenvironment Annotation by Supervised Graph Contrastive Learning

Recent advancements in in situ molecular profiling technologies, including spatial proteomics and transcriptomics, have enabled detailed characterization of the microenvironment at cellular and subcellular levels. While these techniques provide rich information about individual cells spatial coordinates and expression profiles, extracting biologically meaningful spatial structures from the data remains a significant challenge. Current methodologies often rely on unsupervised clustering followed by cell type annotation based on differentially expressed genes within each cluster and most of the time will require other information as the reference (e.g., HE-stained images). This is labor-intensive and demands extensive domain knowledge. To address these challenges, we propose a supervised graph contrastive learning framework, MARVEL. MARVEL is a supervised graph contrastive learning method that can effectively embed local microenvironments represented by cell neighbor graphs into a continuous representation space, facilitating various downstream microenvironment annotation scenarios. By leveraging partially annotated examples as strong positives, our approach mitigates the common issues of false positives encountered in conventional graph contrastive learning. Using real-world annotated data, we demonstrate that MARVEL outperforms existing methods in three key microenvironment-related tasks: transductive microenvironment annotation, inductive microenvironment querying, and the identification of novel microenvironments across different slices.

bioinformatics↗