What happened
Imaging-based spatial transcriptomics enables molecule-resolved profiling of gene expression and tissue organization in situ. However, segmentation errors, transcript spillover and three-dimensional cell overlap can introduce misassigned transcripts into cell-level expression profiles, compromising biological interpretation and obscuring genuine signals. Existing methods either remove suspect expression at the cost of signal loss or lack a biologically grounded criterion for transcript assignment. Here we present CellDot, an optimal-transport framework that determines the fate of each transcript by retaining it in its host cell, reassigning it to a plausible neighboring cell or removing it as background. By integrating reference-guided expression compatibility with spatial information and data-adaptive constraints, CellDot enables accurate and traceable molecule-level correction while preserving biologically meaningful variation. In evaluations across multiple human tumor datasets, CellDot exhibited superior performance compared to existing decontamination methods, successfully restoring spatial expression patterns that matched independent cross-platform measurements. Moreover, it significantly enhanced the recovery of cellular states, intercellular communication, and spatial niche programs. Our experiments using real data demonstrated CellDot's scalability and established it as the only method applicable to a whole-transcriptome Atera dataset, underscoring its distinct advantages in the field of spatial transcriptomics. Update (2026-09-21): Detecting cell segmentation errors using doublet methods . In spatial transcriptomics, cell segmentation is used to draw boundaries around cells. Molecules located within a cell's boundary are assigned to it, making its gene expression profile dependent on segmentation accuracy. To identify potentially problematic cells, studies increasingly use doublet detection methods, a class of algorithms developed to recognize molecular admixture from two cells in scRNA-seq. To evaluate their suitability in spatial data, we model cell segmentation errors as a continuum of partial molecular admixture between neighboring cells, generated by varying the loss of a cell's own transcripts and the gain of transcripts from its neighbor. Evaluating 8 doublet methods across 16 spatial datasets, we characterize the conditions under which they perform well and identify their failure modes. Detection improves with increasing molecular admixture and transcriptional dissimilarity between neighboring cells, with cxds2, scDblFinder.score, and a simple baseline (unique_genes) performing best. These patterns are consistent across datasets spanning different gene panels, technological Update (2026-09-22): MINT infers the latent single-cell spatial transcriptome from paired histology . Sequencing-based spatial transcriptomics captures transcriptome-wide molecular information, yet the corresponding cell-resolved tissue map remains incomplete. Histology records dense cellular morphology, tissue architecture and local context. Here, we present MINT (Morphological Integrative Network for Transcriptomics), which combines this latent cellular organisation with sample-specific spatial measurements to infer tissue-wide, cell-resolved transcriptomes without an external single-cell reference. MINT reconstructs expression from sparse Slide-tags profiles across unmatched adjacent sections; its shared nucleus-centred representation also resolves pooled Visium measurements into cell-level profiles. MINT improved Slide-tags alignment and expression recovery under controlled nuclear loss and cross-section perturbation. It also outperformed reference-free Visium disaggregation methods against Xenium ground truth and was validated with Visium HD sequencing. In human lung adenocarcinoma, MINT reconstructed the tumour immune microenvironment cell by cell and resolved tertiary