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).

• Reciprocal regulation of fibroblast-macrophage equilibrium governs skin integrity.

  PMID 41760904 · PMC13043295 · Nature immunology · 2026 · 8 claims · 8 setups

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  Dpt+ fibroblasts are required to maintain skin macrophage populations in vivo; their depletion reduces CD64+ and CD11c+ macrophages

  facs-sorted skin dpt+ scrna-seq flow cellchat

• Multi-modal skin atlas identifies a multicellular immune-stromal community associated with disrupted cornification and specific T cell expansion in atopic dermatitis.

  PMID 41741455 · PMC13057205 · Nature communications · 2026 · 8 claims · 8 setups

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  Generated a multi-modal single-cell atlas of 280,518 cells from 27 samples/17 adults (healthy, AD non-lesional/lesional, scleroderma), integrated with 430,186 cells from four prior studies into a 710,704-cell human skin atlas with 86 annotated granular cell subsets.

  skin healthy keratinocytes re-annotated droplet-based scrna-seq bulk

• An AI-Enabled Single-Cell Transcriptomic Analysis Pipeline for Gene Signature Discovery in Natural Killer Cells Linked to Remission Outcomes in Chronic Myeloid Leukemia.

  PMID 41972591 · PMC13072394 · Biology · 2026 · 8 claims · 7 setups

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  GAFA integrates latent-space representation, pseudotime trajectory modeling, GRN inference, and machine learning-based gene panel discovery into a single coherent pipeline, unlike existing workflows that treat these steps independently.

  peripheral subset immune assigned scrna-seq latent-space automated

• scDEBGCL: a deep embedding approach based on bipartite graph contrastive learning for single-cell RNA-seq data.

  PMID 41981652 · PMC13188691 · BMC biology · 2026 · 7 claims · 3 setups

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  scDEBGCL is a deep embedding method for scRNA-seq data based on bipartite graph contrastive learning, integrating contrastive learning, graph reconstruction, and ZINB-based data reconstruction losses.

  nine yan dpt scrna-seq trajectory