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).

• Spatial single-cell multiomics reveals peripheral immune dysfunction in Parkinson's and inflammatory bowel disease.

  PMID 41545356 · PMC12847817 · NPJ Parkinson's disease · 2026 · 8 claims · 5 setups

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  Spatial single-cell multiomic imaging of sigmoid colon reveals both shared and unique features of peripheral immune dysfunction in PD and IBD

  sigmoid plasma stool epithelial cosmx meso gene

• Spatial transcriptomics identifies differentiation, lipid metabolism, and retinoid pathway alterations in acne vulgaris.

  PMID 41657309 · PMC12892907 · JCI insight · 2026 · 8 claims · 6 setups

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  A custom KRT5-directed segmentation pipeline improves transcript assignment accuracy in the spatially complex sebaceous gland compared with standard multimodal or nuclei-expansion segmentation

  skin inflammatory tert gtrd spatial immunohistochemistry reanalysis

• Bidirectional CRISPR screens decode a GLIS3-dependent fibrotic cell circuit.

  PMID 41501466 · PMC12820784 · Nature · 2026 · 8 claims · 8 setups

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  Inflammation-associated fibroblasts (IAFs), induced by proinflammatory FCN1+IL1B+ macrophages, produce profibrotic IL-11 and drive fibrosis in IBD

  intestinal colon pdgfra+ colonic scrna-seq xenium masson's

• Integrative CSF profiling identifies disease-specific immune responses in leptomeningeal disease.

  PMID 41794040 · PMC13006398 · Cell reports. Medicine · 2026 · 8 claims · 8 setups

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  CSF exhibits distinct, disease-specific immune landscapes across CNSL, BrMs, and GB-associated LMD

  csf peripheral brain cell-free single-cell deep spatial

• 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