Introduction
Spatial transcriptomics maps gene expression within the spatial context of tissues, providing detailed insights into cellular and molecular landscapes. By retaining the spatial organization of cells, this technology enables researchers to investigate tissue heterogeneity, cellular interactions, and regional specialization at high resolution. It has been widely applied to study processes such as cancer progression, brain development, and disease pathology.
Despite its potential, spatial transcriptomics faces significant challenges. Integrating datasets across different technologies, modalities, and experimental conditions often results in inconsistencies due to variations in resolution, gene panels, and tissue morphology. Existing tools frequently fail to align datasets accurately without sacrificing critical spatial or molecular information, particularly when analyzing complex biological systems. Addressing this issue, a recent study by Tang et al., published in Nature Methods, introduces CAST (Cross-Sample Alignment of Spatial Omics)—a deep graph neural network-based framework designed to align, compare, and analyze spatial datasets at single-cell resolution. CAST ensures precise alignment while enabling analyses that reveal molecular and cellular patterns across diverse samples.
How CAST Enables Spatially Consistent Alignment Across Datasets
The strength of CAST lies in its ability to align spatial datasets from multiple technologies and conditions with high accuracy. The framework comprises three key modules: CAST Mark, CAST Stack, and CAST Projection.
CAST Mark identifies shared spatial features by representing tissue samples as graphs, allowing for the detection of consistent tissue regions without manual annotation. It demonstrates robustness even with noisy or highly variable datasets. For instance, in an Alzheimer’s disease model, CAST Mark successfully aligned eight coronal brain slices from multiple mice, capturing shared regional structures while maintaining cellular organization. The alignments corresponded well with anatomical references, highlighting CAST’s ability to produce biologically meaningful insights. This capability enables integration across platforms like Visium, MERFISH, and Slide-seq, creating a unified framework for cross-study comparisons.
CAST Stack enhances these alignments using a two-step approach: global affine transformation for large-scale alignment followed by B-spline-based local adjustments. This method ensures both global consistency and local precision, making CAST adaptable to datasets with varying resolutions and morphological differences.
Delta Analysis: Mapping Disease-Associated Spatial Features
CAST introduces ΔAnalysis, a novel approach to investigating spatial heterogeneity across samples. By leveraging aligned datasets, ΔAnalysis quantifies local differences in gene expression, cell-type composition, and cell–cell interactions across conditions. This analysis generates detailed spatial maps of molecular changes, offering insights into disease mechanisms.
For example, in an Alzheimer’s disease study, ΔAnalysis identified gene clusters associated with Aβ plaques and p-tau. Microglia-associated genes were overexpressed around Aβ plaques, while changes in oligodendrocytes were observed near p-tau regions. ΔAnalysis also revealed shifts in ligand-receptor interactions, such as Apoe–Trem2 signaling, providing insights into disease-related microenvironmental changes. Notably, ΔAnalysis operates without requiring predefined annotations, making it a versatile tool for exploring disease progression and other biological processes.
Integrating Multi-Omics for Translational Insights
CAST’s third module, CAST Projection, extends its functionality by integrating datasets from different modalities. By combining spatial transcriptomics (RNA expression) with spatial translatomics (ribosome-bound RNA), it enables the analysis of translational regulation at single-cell resolution. This integration uncovers spatial and cellular variations in protein synthesis, yielding new insights into functional specialization.
In a study of mouse brain tissues, CAST Projection revealed region-specific differences in translation efficiency. For example, oligodendrocytes in fiber tracts exhibited increased translation of genes involved in myelination, while thalamic neurons displayed distinct patterns of translation elongation factors. By aligning datasets from STARmap and RIBOmap technologies, CAST Projection preserved spatial and molecular information, enabling the study of multi-omic relationships within tissues.
Conclusion
CAST addresses critical challenges in spatial omics, providing a robust framework for aligning, analyzing, and integrating datasets across modalities and conditions. By leveraging advanced graph neural networks, CAST preserves spatial and molecular integrity, enabling detailed investigations of tissue heterogeneity and disease mechanisms.
Its ΔAnalysis and multi-omics integration capabilities expand the analytical scope of spatial transcriptomics, supporting research into complex biological questions. Whether applied to neurodegenerative diseases, tissue regeneration, or developmental biology, CAST equips researchers with tools to explore spatially resolved molecular insights with precision. As spatial omics technologies advance, CAST represents a significant step forward in bridging methodological gaps and enabling new discoveries.
How Bridge Informatics (BI) Can Support Your Spatial Omics Research
At Bridge Informatics, we specialize in helping life science companies navigate the complexities of cutting-edge technologies like spatial transcriptomics. Our team of data scientists and bioinformaticians expertly selects and applies the most advanced tools, such as CAST, to address your specific research questions with precision and confidence. With a strong foundation in both computational analysis and bench biology, we ensure that our solutions are scientifically rigorous and deeply aligned with your research objectives.
Whether you need support with data integration, pipeline development, or advanced spatial omics analysis, BI is here to help tackle the computational challenges of storing, analyzing and interpreting genomic and transcriptomic data. Ready to advance your research? Click here to schedule a free introductory call with a member of our bioinformatics service provider (BSP) team.
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