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All cells contain essential information in the form of messenger RNA (mRNA), which determines both individual cell function and overall tissue organization. The genetic information encoded in mRNA, known as the transcriptome, can be analyzed using single-cell and spatial transcriptomics.Single-cell transcriptomics provides high-resolution insights into gene expression by analyzing the transcriptome one cell at a time. However, since this method requires cell isolation, it results in a loss of spatial information, including the cell’s original location, neighboring cells, and the interactions contributing to tissue function. These spatial relationships are crucial for understanding tissue organization and cellular behavior, necessitating alternative techniques to preserve them.
Spatial transcriptomics overcomes this limitation by mapping gene expression within intact tissues, offering insights into biological and pathological processes at both the cellular and tissue levels. Cell-cell interactions (CCIs) regulate key processes such as growth, differentiation, and tissue development. Traditional methods for studying CCIs were limited in scale and lacked spatial resolution. However, spatial transcriptomics enables a more comprehensive mapping of these interactions by leveraging ligand-receptor interaction (LRI) inference tools.
Computational approaches for LRI inference involve identifying cell types and statistically assessing potential ligand-receptor (LR) pairs within spatially resolved tissue sections. These methods typically compare observed interactions against curated databases of known ligand-receptor pairs to determine their functional significance. Since CCIs primarily occur between proximal cells, additional filtering steps refine the dataset to identify biologically meaningful interactions.
Several strategies exist for inferring LRIs from spatial transcriptomics data. Correlation-based methods, such as Spearman correlation, assess co-expression of ligand and receptor genes across spatial locations (e.g., ScHOT and SpatialCorr). Alternatively, optimal transport-based algorithms incorporate spatial distance as a cost factor, modeling numerous LR pairs simultaneously, identifying signaling gradients and summarizing directional communication (e.g., COMMOT). Other notable tools include: SpatialDM, testing LR co-expression using a bivariate Moran’s I statistic to measure spatial dependency; Niche-LR, identifying LR signaling that underlies genes differentially expressed in spatially defined niches; and Copulacci, a count-based model accounting for dependencies between the expression of ligands and receptors from nearby spatial locations even when the transcript counts are low.
Each of these methods has distinct advantages and limitations. Correlation-based approaches, like Spearman correlation, are computationally efficient and straightforward, making them useful for rapid assessments of expression patterns. However, they may capture false associations and fail to account for spatial constraints. Optimal transport-based approaches, such as COMMOT, effectively model proximity-dependent interactions and capture directional signaling (e.g., source-to-target) but require precise spatial coordinates and high-quality data and are computationally intensive. Database-driven methods, like SpatialDM, provide results by scanning tissues for known ligand-receptor pairs, but their accuracy depends on the completeness of existing interaction databases, potentially leading to missed interactions.
To systematically evaluate the performance of COMMOT, SpatialDM, Niche-LR, and Copulacci in capturing CCIs during mouse embryogenesis, we will apply these tools to our in-house spatial transcriptomics dataset, which spans critical developmental stages (e.g., gastrulation, organogenesis). Each method will be assessed on its ability to recover known developmental signaling pathways (e.g., Wnt, FGF, Hedgehog) and spatially resolved LR pairs validated in prior literature. For example, COMMOT’s optimal transport framework will be tested for modeling morphogen gradients (e.g., BMP4 in ectoderm patterning), while SpatialDM’s Moran’s I statistic will evaluate spatial autocorrelation of LR pairs like Notch-Delta in somite formation. Performance metrics will include spatial coherence (e.g., overlap with embryonic anatomical landmarks), consistency across biological replicates, and computational efficiency. Challenges such as false positives from auto-/juxtacrine signaling will be mitigated by filtering interactions based on spatial distance thresholds (e.g., excluding pairs within the same cell/spot).
Building on these benchmarks, we will adapt the four tools to model distal interactions, including endocrine and metabolic signaling, which are critical in embryogenesis but poorly captured by existing spatial methods. For endocrine signaling (e.g., hormonal crosstalk between placenta and embryo), we will incorporate ligand diffusion models into COMMOT’s optimal transport framework and extend Niche-LR’s niche definitions to include distal cell types (e.g., hormone-producing cells in extraembryonic tissues). To address metabolic signaling (e.g., nutrient exchange between yolk sac and embryo), we will integrate metabolite spatial imaging data with Copulacci’s copula models to infer dependencies between metabolic enzyme expression and nutrient transporter genes. SpatialDM’s bivariate Moran’s I will be modified to account for long-range correlations (e.g., insulin-like growth factor signaling across tissues). Validation will leverage knockout mouse models with disrupted endocrine/metabolic pathways (e.g., Igf2 mutants) to test predicted interaction losses. By augmenting these tools with spatial multi-omics data and dynamic modeling, our framework will advance the study of both local and systemic interactions in development.
This dual approach will advance spatial transcriptomics beyond niche-centric analysis, providing a unified framework to dissect both local and organism-wide communication driving development.
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