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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VISTA uncovers missing gene expression and spatial-induced information for spatial transcriptomic data analysis.
PMID 41507434 · PMC12891734 · Communications biology · 2026 · 8 claims · 6 setups
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
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Spatial transcriptomics identifies differentiation, lipid metabolism, and retinoid pathway alterations in acne vulgaris.
PMID 41657309 · PMC12892907 · JCI insight · 2026 · 8 claims · 6 setups
A custom KRT5-directed segmentation pipeline improves transcript assignment accuracy in the spatially complex sebaceous gland compared with standard multimodal or nuclei-expansion segmentation
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Spatial gene expression analysis reveals drivers of extremely early lymph node metastasis in breast cancer.
PMID 41578129 · PMC12932634 · NPJ breast cancer · 2026 · 8 claims · 7 setups
Identified 30 isolated tumor cells (ITCs) in a clinically metastasis-negative tumor-draining lymph node, representing the initial metastatic seeding event, spanning ~200 μm
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Resolving sensitivity, specificity and signal contamination in Xenium spatial transcriptomics.
PMID 42062553 · PMC13259927 · Nature methods · 2026 · 8 claims · 6 setups
Xenium data show strong consistency across patients and technical replicates, with little technical variation between platforms
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Has reproduction · 74
SpaGene: A Deep Adversarial Framework for Spatial Gene Imputation.
PMID 42146899 · PMC13176606 · Computational and structural biotechnology journal · 2026 · 8 claims · 6 setups
SpaGene improves average PCC and SSIM and reduces RMSE compared to 6 baseline methods (SpaGE, gimVI, Tangram, VISTA, spRefine, stDiff) across 8 diverse ST-SC dataset pairs under gene-holdout evaluation.
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sCellST predicts single-cell gene expression from H& E images.
PMID 41513659 · PMC12858858 · Nature communications · 2026 · 7 claims · 6 setups
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
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A multi-omic single-cell landscape of perinatal mouse skin maps lineage specification and reveals shared dynamics in human fetal skin.
PMID 41998142 · PMC13144478 · Experimental & molecular medicine · 2026 · 7 claims · 8 setups
Integrated scATAC/scRNA multi-omics analysis of developing mouse skin identifies gene network axes underlying skin lineage specification
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Bidirectional CRISPR screens decode a GLIS3-dependent fibrotic cell circuit.
PMID 41501466 · PMC12820784 · Nature · 2026 · 8 claims · 8 setups
Inflammation-associated fibroblasts (IAFs), induced by proinflammatory FCN1+IL1B+ macrophages, produce profibrotic IL-11 and drive fibrosis in IBD
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Interpretable, flexible and spatially aware integration of multiple spatial transcriptomics datasets from diverse sources.
PMID 42045691 · PMC13175893 · Nature genetics · 2026 · 6 claims · 7 setups
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
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PreTSA: computationally efficient modeling of temporal and spatial gene expression patterns.
PMID 41673899 · PMC12998178 · Genome biology · 2026 · 7 claims · 8 setups
PreTSA dramatically reduces computational time and memory versus GAM (Monocle, TSCAN) and PseudotimeDE for identifying temporally variable genes (TVGs) while producing highly similar results
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FineST: contrastive learning integrates histology and spatial transcriptomics for nuclei-resolved ligand-receptor analysis.
PMID 41839892 · PMC13201544 · Nature communications · 2026 · 8 claims · 6 setups
FineST, a bimodal contrastive learning model integrating histology (Virchow2 ViT features) and spatial gene expression, enables nuclei-resolved high-resolution RNA imputation.