Quantitative characterization of cell niches in spatial atlases

Tissues are composed of heterogeneous cell niches, which can be investigated using spatial omics technologies. Large consortia have accumulated vast amounts of spatially resolved data, which typically assign slice-level condition labels without considering intra-slice heterogeneity, particularly differential cell niches that respond to certain perturbations. Here, we present Taichi, an efficient and scalable method for condition-relevant cell niche analysis that does not rely on pre-defined discrete spatial clustering. Taichi utilizes a scalable spatial co-embedding approach that effectively accounts for batch effects, incorporating advanced label refinement and graph heat diffusion techniques to explore condition-relevant cell niches across extensive multi-slice and multi-condition spatial omics datasets. Comprehensive benchmarks demonstrate Taichi’s ability to precisely identify condition-relevant niches under various levels of perturbations. We showcase Taichi’s effectiveness in accurately delineating major shifts in cell niches in a mouse model of diabetic kidney disease compared to a normal group, revealing disease-specific cell-cell interactions and spatial gene expression patterns. Furthermore, Taichi can identify key subtype-relevant niches between colorectal cancer patient groups with significantly different survival outcomes. Moreover, we demonstrate that Taichi can help discover more fine-grained clinical properties within the originally coarse-defined patient groups in large-scale tumor spatial atlases, reflecting intra-group heterogeneity obscured previously. Additionally, we combine Taichi and tensor decomposition to discover higher-order biomarkers relevant to the immunotherapy response of triple-negative breast cancer. Finally, we highlight Taichi’s speed and scalability by confirming its unique applicability in large-scale scenarios containing up to 16 million cells in ∼ 12 minutes. Taichi provides a powerful tool for mining disease-relevant spatially resolved insights in the era of big data in spatial biology.

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Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep Learning

Cui,

Yuan

2024

Preprint

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Spatially resolved transcriptomics technologies enable the mapping of multiplexed gene expression profiles within tissue contexts. To explore the gene spatial patterns in complex tissues, computational methods have been developed to identify spatially variable genes within single tissue slices. However, there is a lack of methods designed to identify genes with differential spatial expression patterns (DSEPs) across multiple slices or conditions, which becomes increasingly common in complex experimental designs. The challenges include the complexity of cross-slice gene expression and spatial information modeling, scalability issues in constructing large-scale cell graphs, and mixed factors of inter-slice heterogeneity. We propose DSEP gene identification as a new task and develop River, an interpretable deep learning-based method, to solve this task. River comprises a two-branch prediction model architecture and a post-hoc attribution method to prioritize DSEP genes that explain condition differences. River’s special design for modeling spatial-informed gene expression makes it scalable to large-scale spatial omics datasets. We proposed strategies to decouple the spatial and non-spatial components of River’s outcomes. We validated River’s performance using simulated datasets and applied it to identify DSEP genes/proteins in diverse biological contexts, including embryo development, diabetes-induced alterations in spermatogenesis, and lupus-induced splenic changes. In a human triple-negative breast cancer dataset, River identified generalizable survival-related DSEPs, validated across unseen patient groups. River does not rely on specific data distribution assumptions and is compatible with various spatial omics data types, making it a versatile method for analyzing complex tissue architectures across multiple biological conditions.

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Quantitative characterization of cell niches in spatial atlases

Cited by 4 publications

References 173 publications

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics

Scalable Condition-relevant Cell Niche Analysis of Spatial Omics Data with Taichi

Identifying Differential Spatial Expression Patterns across Different Slices, Conditions and Developmental Stages with Interpretable Deep Learning

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