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Single cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells

ABSTRACT: T cell mediated rejection (TCMR) affects ∼50% of pediatric liver transplant recipients within 5 years, and late TCMR is associated with graft failure due to ineffective treatment. Although CD8+ T cells promote late TCMR, their clonal expansion, intragraft persistence, localization, and gene expression remain largely undefined. Herein, single-cell RNA sequencing and immune repertoire profiling of 30 cryopreserved liver biopsies from rejecting and non-rejecting pediatric patients identified expanded intragraft CD8+ T cell clonotypes (CD8EXP) and their gene expression profiles. Expanded CD8+ clonotypes (CD8EXP) bore markers of effector and CD56hiCD161- 'NK-like' T cells, retaining their clonotype identity and phenotype in serial biopsies despite histologic TCMR resolution. CD8EXP clonotypes localized to portal infiltrates during active TCMR and persisted in the lobule after histologic TCMR resolution. MultiNicheNet analysis revealed differential crosstalk between Kupffer cells (KC) and CD8EXP, with activation of TGFß and downregulation of CD47 immune checkpoint signaling. Therefore, persistenting intragraft CD8EXP clones remain active despite TCMR treatment and may contribute to long-term allograft fibrosis and failure of operational tolerance.

ORGANISM(S): Homo sapiens

PROVIDER: GSE256141 | GEO | 2025/08/20

REPOSITORIES: GEO

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Project description:Background: Biopsy-based diagnosis is essential for maintaining renal allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods. Methods: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from renal transplant patients, comprising five biopsies with the diagnosis of T-cell-mediated rejection (TCMR), five biopsies for polyomavirus BK nephropathy (BKPyVN), and normal kidney control tissue (STA). Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. Results: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using isobaric labeling technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yields 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. Conclusions: This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of quantitative label-free mass spectrometry analysis with a machine learning-derived diagnostic model. It costs less than 10 dollars per test.

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Single cell transcriptional landscape of liver transplant rejection reveals tissue persistence of clonally expanded, treatment-resistant T cells

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