Optimizing the design of spatial genomic studies

Abstract

Spatial genomic technologies characterize the relationship between the structural organization of cells and their cellular state. Despite the availability of various spatial transcriptomic and proteomic profiling platforms, these experiments remain costly and labor-intensive. Traditionally, tissue slicing for spatial sequencing involves parallel axis-aligned sections, often yielding redundant or correlated information. We propose structured batch experimental design, a method that improves the cost efficiency of spatial genomics experiments by profiling tissue slices that are maximally informative, while recognizing the destructive nature of the process. Applied to two spatial genomics studies-one to construct a spatially-resolved genomic atlas of a tissue and another to localize a region of interest in a tissue, such as a tumor-our approach collects more informative samples using fewer slices compared to traditional slicing strategies. This methodology offers a foundation for developing robust and cost-efficient design strategies, allowing spatial genomics studies to be deployed by smaller, resource-constrained labs.

Fig. 1. Demonstration of slicing in two-dimensional simulated tissue.

a Simulated spherical tissue with a grid of spots. b An example one-dimensional slice through the tissue. c The resulting observations at each spot after taking the slice in (b). The colors represent a univariate phenotype. d After slicing, the simulated tissue is split into two fragments. e Each line represents a candidate one-dimensional slice. Each slice is colored by its EIG (normalized to have a maximum of one). The slice with the highest EIG is then chosen; see panel (f). f The EIG-maximizing slice from the candidates in panel (e). g Tissue fragments after T = 10 iterations of repeated slicing. Each color represents a distinct fragment. Source data are provided as a Source Data file.

Fig. 2. Imputing unobserved gene expression from observed cross-sections.

a Simulated tissue colored by synthetic gene expression. b An example slice through the synthetic tissue. c The resulting observations from the slice in (b). d R² for gene expression imputation after each slicing iteration for each method. Error bands represent 95% confidence intervals computed using n = 5 runs. Source data are provided as a Source Data file.

Fig. 3. Synthetic slicing experiment for localizing a region of interest.

a Two-dimensional simulated spatial gene expression data with a region of interest in orange (ROI). b Point-wise observations. Orange points are labeled as belonging to the ROI, blue points are outside the ROI, and gray points are unobserved. c Estimated expected information gain (EIG) for each spatial location, where each design is a single point. d Estimated EIG for each horizontal slice design. e Synthetic ROI data. f Slices chosen after T = 5 iterations of running our model. g Mean F1 score of predictions after each iteration. Error bars represent 95% confidence intervals computed using n = 5 runs. Source data are provided as a Source Data file.

Fig. 4. Application to Visium data.

a Spatial locations of tissue. b Slices chosen by each approach after T = 5 iterations. The outline of the tissue is shown by the solid black line, and the slices chosen by each approach are shown by the dashed lines. The color legend is in panel (c). The full lines are drawn for clarity, but only the nonintersecting piece (within one tissue fragment) is considered as the relevant slice. c Predictive R² of the held-out gene expression for both approaches across iterations. Error bands represent 95% confidence intervals computed using n = 5 runs. Source data are provided as a Source Data file.

Fig. 5. Reconstructing the Allen Brain Atlas.

a Allen Brain Atlas coordinates colored by the expression of PCP4. b An example slice through the coordinates. c The resulting observations after taking this slice. d The slices and observations chosen by the EIG approach. e Imputation performance across experimental iterations. Error bars represent 95% confidence intervals computed using n = 5 runs. Source data are provided as a Source Data file.

Fig. 6. Localizing invasive carcinoma in prostate tissue.

a Slices chosen by the EIG method. Cancerous spots are shown in red. The full lines are drawn for clarity, but only the nonintersecting piece (within one tissue fragment) is considered as the relevant slice. b F1 score of tumor/healthy label predictions after each iteration of experimental design. Error bars represent 95% confidence intervals computed using n = 5 runs. c Tumor/healthy predictions following five iterations of design. Stronger yellow color indicates spots with higher predicted probability of containing tumorous tissue. Source data are provided as a Source Data file.