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).

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

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

  Full-text index only

  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

• Spatial transcriptomics maps host-gut microbiome biogeography at high resolution.

  PMID 41792309 · PMC13171632 · Nature microbiology · 2026 · 7 claims · 6 setups

  Full-text index only

  Enzymatic in situ polyadenylation increases bacterial RNA recovery in oligo(dT)-based spatial transcriptomics arrays by up to ~100-fold while preserving host gene capture

  apc-deficient heart ileum fresh-frozen spatial high-resolution bulk

• STAN, a computational framework for inferring spatially informed transcription factor activity.

  PMID 41521668 · PMC12784991 · Nucleic acids research · 2026 · 7 claims · 7 setups

  Full-text index only

  STAN, a linear mixed-effects (spatially weighted regression) model, integrates TF-target gene priors, gene expression, spatial coordinates, and histological image features to predict spot-specific TF activity in spatial transcriptomics data

  lymph glioblastoma breast dorsolateral spatial scrna-seq