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).
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Tree shrew immune cell atlas identifies NR1H3⁺ tissue macrophages with conserved anti-inflammatory function.
PMID 41957356 · PMC13223331 · Nature communications · 2026 · 8 claims · 8 setups
A single-cell/single-nucleus RNA-seq atlas across 12 tree shrew tissues identifies 39 distinct immune and non-immune cell types, providing a reference resource for tree shrew immunology
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A Transcriptional Map of Human Tonsil Architecture: Beyond the Sum of (Single Cell) Parts.
PMID 41518352 · PMC12790319 · European journal of immunology · 2026 · 8 claims · 5 setups
Imaging-based spatial transcriptomics (CosMx SMI) captures tissue complexity and cellular composition more faithfully than scRNA-seq
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Single-cell analysis reveals multi-faceted features of B cell development, together with age-associated B cell subpopulations.
PMID 41520091 · PMC12905261 · Communications biology · 2026 · 8 claims · 8 setups
18 B cell subpopulations were identified spanning the full developmental continuum from early progenitors to plasma cells, including 4 previously unappreciated minor subpopulations (FTLhi, S100A8hi immature; FOShi, HSPA1Ahi naïve).
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Spatial transcriptomics unveils immune cellular ecosystems associated with patient survival in diffuse large B-cell lymphoma.
PMID 42010788 · PMC13102037 · Oncoimmunology · 2026 · 7 claims · 7 setups
DLBCL tissues are organized into six recurrent, spatially defined cellular ecosystems (Cell-Eco) with distinct immune compositions, transcriptional programs, and neighborhood architectures.
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STAN, a computational framework for inferring spatially informed transcription factor activity.
PMID 41521668 · PMC12784991 · Nucleic acids research · 2026 · 7 claims · 7 setups
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