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Jinze Liu

Publications and source records attributed to Jinze Liu.

6 recordsLinked to original sources
PubMed2026

An integrated single-cell and spatial proteotranscriptomics atlas of fibroblast-driven immunoregulation within the human adult oral cavity.

The immunoregulatory architecture of human oral tissues remains poorly defined. We present an integrated single-cell and spatial proteotranscriptomic atlas profiling >250,000 single-cell transcriptomes and >4 million spatially resolved cells across 13 niches. Using our AI-enabled AstroSuite, we defined neighborhoods and interaction modules, revealing peri-epithelial fibroblast-centered hubs enriched in effector cytokines. We harmonized fibroblast subtypes (universal, immune, peri-epithelial, peri-vascular, peri-neural, antigen-presenting cell [APC]-like, stress responsive, and myofibroblasts) with stress-responsive subtypes partitioning between mucosae and glands (type I and II). Spatial multiomics mapped ligand-receptor programs and identified mucosal stress-responsive fibroblasts as putative immunoregulatory hubs. Niche-aware integration of healthy and diseased datasets revealed fibroblast rewiring into inflammatory and reparative niches. Disease neighborhoods exhibited expansion of major histocompatibility complex (MHC)-I+, MHC-II+, and programmed cell death ligand 1 (PD-L1)+ fibroblasts and predicted spatial engagement with T cells at tertiary lymphoid structures. Together, this atlas identifies fibroblasts as central regulators of structural immunity and provides a scalable framework to target stromal-immune interactions across barrier organs.

Journal Article↗
PubMed2025

GZMK+CD8+ T cells target a specific acinar cell type in Sjögren's disease.

OBJECTIVES: Sjögren's disease (SjD) is a systemic autoimmune disorder characterized by dysfunction of exocrine glands, particularly the salivary and lacrimal glands, with no clear etiology or effective therapy. This study explores the complex interplay of varied cell types in the salivary glands and their role in the pathology of Sjögren's disease. METHODS: Utilizing single-cell and spatial transcriptomics alongside spatial immunophenotyping to analyze human minor salivary glands, we developed a comprehensive understanding of the cellular landscape of non-SjD salivary glands and how that landscape changes in SjD patients. In vitro cellular assays and novel patient-derived primary epithelial cells were co-cultured with autologous T cells to confirm effector states and the delivery and effect of disease-associated granzymes. RESULTS: We identified previously unrecognized heterogeneity among acinar cells, including a PRR4⁺CST3⁺WFDC2⁻ seromucous acinar population that is selectively lost in Sjögren's disease. Expression and organizational changes were linked to clinical features: (i) T cells in the glands of SSA⁺, high-focus score patients showed increased transcriptional signatures of activation, antigen presentation, and apoptosis resistance compared with patients with mild or moderate disease, and (ii) patients with low immune infiltration exhibited distinct epithelial organization. Notably, GZMK⁺CD8⁺ T cells, which accumulate with disease severity, displayed a cytotoxic transcriptional program, degranulated upon stimulation ex vivo, and localized spatially with immune-engaged epithelial cells. Functional assays demonstrated that GZMK activates interferon signaling in vitro, and autologous co-cultures of patient-derived T cells and epithelial cells validated these findings. CONCLUSIONS: Using single-cell and spatial transcriptomics and proteomics, this study identifies a selective loss of PRR4⁺CST3⁺WFDC2⁻ seromucous acinar cells and a rise in GZMK⁺CD8⁺ T cells in Sjögren's disease, revealing distinct immune-mediated epithelial remodeling and interferon-driven dysfunction across diverse clinical presentations. These findings uncover a novel sub-cytolytic effector mechanism by which GZMK⁺CD8⁺ T cells impair mitochondrial integrity and activate innate immune signaling, linking epithelial injury to type I interferon responses and offering new therapeutic targets.

Humans↗
PubMed2025

Anthracyclines attenuate Nrf1-dependent proteolytic pathways and potentiate proteasome inhibitor cytotoxicity.

Proteasome inhibitors such as bortezomib, carfilzomib, and ixazomib are FDA-approved treatments for multiple myeloma, but resistance frequently limits their effectiveness. The transcription factor Nrf1 (NFE2L1) upregulates proteasome and autophagy genes upon proteasome inhibition, contributing to adaptive resistance. In this study, we identified anthracyclines, including doxorubicin, as suppressors of the Nrf1-driven transcriptional response. Mechanistically, doxorubicin impaired Nrf1 binding to antioxidant response elements (AREs) within promoter regions of target genes without affecting Nrf1 processing or nuclear localization. Importantly, aclarubicin, a non-DNA-damaging anthracycline, also attenuated Nrf1 transcriptional activity, indicating that DNA damage is not required for this inhibition. Doxorubicin cotreatment delayed proteasome recovery after pulse inhibition and partially restored sensitivity to carfilzomib in bortezomib-resistant U266 myeloma cells, consistent with genetic knockout of Nrf1. These findings identify a DNA-damage-independent mechanism by which anthracyclines directly obstruct Nrf1-mediated transcriptional induction. Thus, anthracyclines serve as chemical tools to probe the molecular control of proteostasis and suggest a strategy to mitigate Nrf1-driven adaptive response to proteasome inhibition.

Humans↗
PubMed2006

Structure-based function inference using protein family-specific fingerprints.

We describe a method to assign a protein structure to a functional family using family-specific fingerprints. Fingerprints represent amino acid packing patterns that occur in most members of a family but are rare in the background, a nonredundant subset of PDB; their information is additional to sequence alignments, sequence patterns, structural superposition, and active-site templates. Fingerprints were derived for 120 families in SCOP using Frequent Subgraph Mining. For a new structure, all occurrences of these family-specific fingerprints may be found by a fast algorithm for subgraph isomorphism; the structure can then be assigned to a family with a confidence value derived from the number of fingerprints found and their distribution in background proteins. In validation experiments, we infer the function of new members added to SCOP families and we discriminate between structurally similar, but functionally divergent TIM barrel families. We then apply our method to predict function for several structural genomics proteins, including orphan structures. Some predictions have been corroborated by other computational methods and some validated by subsequent functional characterization.

Bacterial Proteins↗
PubMed2004

Biclustering in gene expression data by tendency.

The advent of DNA microarray technologies has revolutionized the experimental study of gene expression. Clustering is the most popular approach of analyzing gene expression data and has indeed proven to be successful in many applications. Our work focuses on discovering a subset of genes which exhibit similar expression patterns along a subset of conditions in the gene expression matrix. Specifically, we are looking for the Order Preserving clusters (OPCluster), in each of which a subset of genes induce a similar linear ordering along a subset of conditions. The pioneering work of the OPSM model[3], which enforces the strict order shared by the genes in a cluster, is included in our model as a special case. Our model is more robust than OPSM because similarly expressed conditions are allowed to form order equivalent groups and no restriction is placed on the order within a group. Guided by our model, we design and implement a deterministic algorithm, namely OPCTree, to discover OP-Clusters. Experimental study on two real datasets demonstrates the effectiveness of the algorithm in the application of tissue classification and cell cycle identification. In addition, a large percentage of OP-Clusters exhibit significant enrichment of one or more function categories, which implies that OP-Clusters indeed carry significant biological relevance.

Algorithms↗
PubMed2004

Gene Ontology friendly biclustering of expression profiles.

The soundness of clustering in the analysis of gene expression profiles and gene function prediction is based on the hypothesis that genes with similar expression profiles may imply strong correlations with their functions in the biological activities. Gene Ontology (GO) has become a well accepted standard in organizing gene function categories. Different gene function categories in GO can have very sophisticated relationships, such as 'part of' and 'overlapping'. Until now, no clustering algorithm can generate gene clusters within which the relationships can naturally reflect those of gene function categories in the GO hierarchy. The failure in resembling the relationships may reduce the confidence of clustering in gene function prediction. In this paper, we present a new clustering technique, Smart Hierarchical Tendency Preserving clustering (SHTP-clustering), based on a bicluster model, Tendency Preserving cluster (TP-Cluster). By directly incorporating Gene Ontology information into the clustering process, the SHTP-clustering algorithm yields a TP-cluster tree within which any subtree can be well mapped to a part of the GO hierarchy. Our experiments on yeast cell cycle data demonstrate that this method is efficient and effective in generating the biological relevant TP-Clusters.

Algorithms↗