bioRxivImmunologyPreprint
Blood-to-tissue translation in autoimmune disease: paired single-cell evidence from systemic sclerosis
Abstract
AO_SCPLOWBSTRACTC_SCPLOWThe biology that governs progression and therapeutic response in autoimmune disease is organized in affected tissue, but direct molecular readout of that biology requires invasive biopsy and is rarely repeated during clinical trials or routine care. Using paired blood-skin single-cell RNA-sequencing from a systemic sclerosis (SSc) cohort of 74 individuals (57 patients and 17 matched controls, 192,809 cells across 53 annotated cell states), we show that peripheral blood carries a recoverable projection of tissue-resident molecular state. Across 63 pathways scored in both compartments, 43 same-pathway blood-skin associations reach FDR < 0.05; at cell-type resolution, 212 cross-compartment associations survive residualization for disease status and sex. Per-patient classifiers recover tissue-defined molecular states out of fold with AUCs between 0.62 and 0.79, with the strongest recoveries on fibroblast subtype programs that have no direct circulating analog: fibroblast COMP at 0.79, COCH at 0.75, MYOC2 at 0.74, POSTN at 0.74. Tissue programs route through different blood compartments at different representational levels: fibroblast programs resolve through T-cell, Treg, monocyte and B-cell axes at compositional and distributional levels, while interferon resolves through expression state across multiple cell types. Within SSc alone, a cross-validated partial least squares model learns a shared blood-skin latent axis at r = 0.486 (permutation p = 0.006); the induced patient ranking recovers tissue-interferon-high patients at 86% precision at the top-20% screening threshold against a 50% base rate. A paired multiview autoencoder, trained on module-level dependency structure under contrastive alignment, paired reconstruction, neighborhood preservation and tissue-target supervision, learns a shared latent geometry in which blood-only projections land in the same tissue-state region as their matched tissue samples and supports recovery of held-out tissue targets above simpler baselines and above two permutation null families. These results map the empirical geometry of cross-compartment inference in autoimmune disease and position peripheral blood as a substrate for tissue-state inference at trial and clinical scale.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: clustering
- research focus: dimensionality reduction
- approach: deep learning
- approach: contrastive learning
- Citations
- 0
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