SPARKLE: evidence-constrained correction of local RNA leakage in high-resolution spatial transcriptomics

High-resolution sequencing-based spatial transcriptomics, including Stereo-seq and Visium HD, aggregates dense capture units into cell-resolved expression matrices. During tissue processing and permeabilization, RNA released from source cells can spread to neighbouring capture locations, reducing cell-type specificity and biasing downstream analyses. Here we developed SPARKLE (Spatial Ambient RNA Kernel-based Leakage Estimator), a cell-level correction method that uses capture locations outside cell-segmentation masks as within-sample spatial evidence of leakage. SPARKLE fits sparse spatial kernels to out-of-mask observations to estimate a sample-level propagation scale and gene-specific leakage coefficients. It corrects only genes supported by out-of-mask goodness of fit and uses expression-dependent conservative shrinkage to protect highly expressing source cells. Across ten simulated scenarios, SPARKLE achieved the highest cell-wise concordance and the lowest RMSE in 9 of 10 scenarios. In axolotl brain, mouse brain and human ovarian cancer, SPARKLE removed ectopic marker signal from neighbouring cells while retaining source-cell expression, improved agreement with independent single-cell and single-nucleus references, and recovered COL1A2–SDC4 signaling of fibroblast origin that collagen diffusion had obscured. Conclusions remained stable across plausible spatial scales and background-bin sizes. Runtime scaled near-linearly with tissue-window area, and was further acceleration on GPU. SPARKLE is therefore a reference-free, fast and scalable method for correcting local RNA leakage from evidence contained within each sample, improving the reliability of cell-type localization, tissue-compartment identification and cell-cell communication inference.