Experiments

Searchable full-text extractions: founding hypothesis, core claims, experimental setups, key results and statistics — pulled out of each paper as structure. Search a cell line, an assay or an entity (e.g. HUH7) and find every paper that worked with it. This corpus stands on its own: most entries carry no reproduction assessment (yet).

• Reconstructing single-cell resolution from spatial transcriptomics with CellRefiner.

  PMID 41760664 · PMC13066420 · Nature communications · 2026 · 8 claims · 8 setups

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  CellRefiner is a physical/particle-based model (subcellular element method) that integrates scRNA-seq and spatial transcriptomics data to reconstruct single-cell resolution spatial data

  hypothalamic embryo hippocampus visual merfish seqfish slide-seqv2

• Smart spatial omics (S2-omics) optimizes region of interest selection to capture molecular heterogeneity in diverse tissues.

  PMID 41298871 · PMC12662399 · Nature cell biology · 2025 · 7 claims · 6 setups

  Has reproduction · 100

  S2-omics is an end-to-end workflow that automatically selects ROIs from H&E histology images to maximize molecular information content for spatial omics profiling.

  gastric colorectal additional breast xenium visium histology

• Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.

  PMID 42045691 · PMC13175893 · Nature genetics · 2026 · 6 claims · 7 setups

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  INSPIRE is a deep-learning method that unifies adversarial learning with a GNN-based encoder and integrated NMF to interpretably integrate multiple spatial transcriptomics datasets

  synthetic dorsolateral brain cortex simulated visium spatial

• VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.

  PMID 41507434 · PMC12891734 · Communications biology · 2026 · 8 claims · 6 setups

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  VISTA predicts unmeasured gene expression in subcellular spatial transcriptomic data by integrating scRNA-seq and SST through variational inference and geometric deep learning with built-in uncertainty quantification

  brain breast embryo muscle imaging-based sequencing spot-based

• FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.

  PMID 41839892 · PMC13201544 · Nature communications · 2026 · 8 claims · 6 setups

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  FineST, a bimodal contrastive learning model integrating histology (Virchow2 ViT features) and spatial gene expression, enables nuclei-resolved high-resolution RNA imputation.

  colorectal crc breast he-stained spatial type ccc

• sCellST predicts single-cell gene expression from H& E images.

  PMID 41513659 · PMC12858858 · Nature communications · 2026 · 7 claims · 6 setups

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  sCellST is a weakly supervised (Multiple Instance Learning) deep learning framework that predicts single-cell gene expression from H&E images alone, trained using paired spatial transcriptomics (Visium) and H&E slides

  ovarian kidney prostate breast simulation spot-level nucleus

• PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.

  PMID 41673899 · PMC12998178 · Genome biology · 2026 · 7 claims · 8 setups

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  PreTSA dramatically reduces computational time and memory versus GAM (Monocle, TSCAN) and PseudotimeDE for identifying temporally variable genes (TVGs) while producing highly similar results

  simulated null pbmc pancreas pseudotime tvg false-positive