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Promoting astrocyte-neuron triiodothyronine shuttling attenuates brain damage in neonatal hypoxic-ischemic encephalopathy.
BACKGROUND: Neonatal Hypoxic-Ischemic Encephalopathy (HIE) is a critical lifelong disabling neonatal disease with insufficient effective therapies. Signaling molecules derived from Astrocyte-Neuron Crosstalk have the potential to improve the prognosis, but relevant HIE research is limited. METHODS: We established hypoxic–ischemic brain damage (HIBD) in postnatal day 7 rat pups using the Rice–Vannucci model, and verified the injury both structurally and hemodynamically by histopathology and laser speckle contrast imaging. Whole-brain single-cell RNA sequencing was performed at P10, and proteomic profiling was conducted on astrocytes subjected to oxygen–glucose deprivation. We charted astrocyte diversity in situ using multiplex immunofluorescence. Thyroid hormones and signaling pathways were quantified by ELISA and Western blotting. Therapeutic efficacy was assessed by histopathology and the Morris water maze. RESULTS: A brain-wide single-cell transcriptomic atlas revealed a strong transcriptional coupling between astrocytes and neurons. Among astrocytes, we identified and functionally characterized a Dio2⁺Slc1a2⁺ subpopulation with metabolic and neuroprotective features, which was markedly depleted after HIBD. Pseudotime trajectories showed dynamic astrocyte state transitions over time, contextualizing the loss and reparative potential of the Dio2⁺ subpopulation. In vivo and in vitro, boosting astrocytic Dio2 or delivering exogenous Triiodothyronine(T3) enhanced neural repair and improved behavior across both early and long-term stages. CONCLUSIONS: We defined a previously unrecognized neuroprotective astrocyte subpopulation and revealed temporal heterogeneity in astrocytic inflammatory responses in HIE. We also identified a T3-mediated astrocyte–neuron crosstalk mechanism that mitigated HIBD, highlighting a tractable therapeutic avenue.
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Integrated transcriptomic and functional analyses identify PRR7 as a therapeutic target that associated with Wnt/β-catenin signaling and docetaxel resistance in prostate cancer.
BACKGROUND: Docetaxel resistance represents a major therapeutic challenge in advanced prostate cancer. Despite extensive studies on drug efflux, apoptosis, and metabolic adaptation, the specific cellular states and molecular determinants that sustain resistance remain poorly understood. METHODS: To delineate the cellular basis of chemoresistance, we integrated bulk RNA sequencing of docetaxel-resistant prostate cancer models with single-cell transcriptomic data from patient tumors. Resistance-associated gene signatures were projected onto a single-cell atlas to identify docetaxel-resistant associated cancer cells (DRACs). Candidate regulators were screened through cross-dataset analysis and validated using molecular, cellular, and organoid-based assays, with functional and signaling consequences evaluated across in vitro and in vivo systems. RESULTS: Single-cell mapping revealed a distinct epithelial cluster characterized by enrichment of resistance-related transcriptional programs and extensive communication with stromal cells. Integrative analyses identified PRR7 as a consistently upregulated gene within DRACs and resistant cell lines. PRR7 amplification and co-alteration with PI3K-AKT and Wnt pathway components were observed in clinical datasets. Functional inhibition of PRR7 suppressed β-catenin signaling, reduced cell viability, and downregulated stemness markers in docetaxel-resistant cells and patient-derived organoids. These results support a functional association between PRR7 and Wnt/β-catenin-related maintenance of resistance. CONCLUSION: The study defines DRACs as a transcriptionally distinct subpopulation sustaining docetaxel tolerance and identifies PRR7 as a regulatory hub connecting genomic alterations, Wnt/β-catenin activation, and stem-like phenotypes. These findings provide a framework for understanding chemoresistance in prostate cancer and suggest PRR7-centered signaling as a potential target for therapeutic intervention.
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Increased expression of tribbles homolog 3 predicts poor prognosis and correlates with tumor immunity in clear cell renal cell carcinoma: a bioinformatics study.
Tribbles homolog 3 (TRIB3), a pseudokinase that regulates multiple intracellular signaling pathways, has been reported to promote the growth of multiple tumors. However, its role in clear cell renal cell carcinoma (ccRCC) remains unelucidated. We evaluated the role of TRIB3 in ccRCC using publicly available data from The Cancer Genome Atlas and analyzed its relationship with the tumor microenvironment; moreover, we used gene knockout and overexpression techniques to detect the effects of TRIB3 on the biological behavior of ccRCC cells. RT-qPCR and western blotting were used to detect transfection efficiency, and the invasiveness of ccRCC cells was determined by Transwell migration assays. We found that TRIB3 overexpression was significantly associated with increased grade, stage, and distant metastasis, positively correlated with ccRCC invasiveness, and also an independent risk factor for overall survival (OS). In addition, 361 differentially expressed genes (DEGs) related to TRIB3 were identified. Functional enrichment analysis showed that DEGs were mainly enriched in humoral immune responses, collagen-containing extracellular matrix, and serine hydrolase activity. Immune landscape characterization revealed that TRIB3 expression was significantly and negatively associated with CD8+ T and hematopoietic stem cells, whereas it was positively associated with NK T and macrophage M1 cells. Single-cell sequencing showed that localization and binding targets of TRIB3 mainly involved monocytes/macrophages and CD4+ and CD8+ T cells. Overall, our study revealed that elevated TRIB3 expression represents a promising prognostic marker for ccRCC patients and may play a key role in tumor microenvironment modulation.
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Multi-omics comparison of periodontitis and peri-implantitis identifies plasma cell enrichment as a shared feature and a periodontitis-associated endothelial-plasma cell APP-CD74 axis.
OBJECTIVE: To define the shared and divergent immune architecture of periodontitis (PD) and peri-implantitis (PI) and to identify intercellular communication programs linked to plasma cell accumulation. METHODS: Differentially expressed genes (DEGs) in PD and PI versus healthy controls were identified from two bulk datasets (GSE223924, GSE106090); robust cross-dataset common DEGs underwent functional enrichment and protein-protein interaction analysis. A single-cell atlas (GSE171213) supported dual deconvolution (BisqueRNA, CIBERSORTx) and CellChat-based, plasma cell-centered communication inference. Key findings were independently validated in two additional periodontitis bulk cohorts (GSE10334, GSE16134) and an independent periodontitis single-cell dataset (GSE164241). Candidate ligand-receptor interactions were evaluated by spatial transcriptomics (GSE206621) and multiplex immunofluorescence in a rat periodontitis model. RESULTS: PI showed broader transcriptomic perturbation than PD (1,832 PI-specific and 820 PD-specific cross-validated DEGs). A total of 693 robust common DEGs with 99.0% directional concordance defined a shared inflammatory core enriched in cytokine signaling, B-cell activation, and osteoclast differentiation. Single-cell analysis resolved 15 cell populations and marked immune remodeling in periodontitis. Dual deconvolution identified plasma cell enrichment as the most reproducible infiltration feature across datasets and methods. CellChat revealed disease-associated remodeling of the plasma cell communication network, with endothelial cells as dominant senders and APP-CD74 as the strongest candidate incoming axis. Spatial transcriptomics showed a 14.43-fold increase in spot-level APP-CD74 co-enrichment in periodontitis-affected tissue, and multiplex immunofluorescence supported enhanced protein-level coupling between APP+CD31+ endothelial and CD74+CD138+ plasma cell domains. CONCLUSIONS: Plasma cell enrichment is the most robust shared immune feature of PD and PI. In periodontitis, convergent multi-omics and tissue-level evidence supports a candidate endothelial-plasma cell communication program centered on APP-CD74, suggesting that endothelial-plasma cell niche remodeling may sustain periodontal inflammation and inform host-modulatory strategies. Importantly, this axis was established only in periodontitis; its relevance to peri-implantitis remains inferential and will require direct single-cell and spatial validation in peri-implant tissue.
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Dynamic transcriptional immune landscape in response to NK-cell therapy combined with gemcitabine plus S-1 in advanced pancreatic cancer: a phase 1b/2 trial.
Despite advancements in several malignancies, the treatment atlas of natural killer (NK) cell therapy for pancreatic cancer remains inadequate, and the dynamic immune landscape underlying the various responses is still incompletely understood. This phase 1b/2 trial evaluated the safety and efficacy of allogeneic NK cell therapy combined with gemcitabine and S-1 as a first-line treatment for advanced pancreatic cancer (APC) and explored the dynamic responsive immune landscape (ChiCTR1900021764). The administration of 1 × 109 to 8 × 109 NK cells to 24 patients was well tolerated, with no graft-versus-host disease or dose-limiting toxicity. Among the 19 evaluable patients, the objective response rate was 31.6%, and the disease control rate was 73.7%. The median progression-free survival was 6.6 months, and the overall survival was 10.8 months. Further longitudinal single-cell RNA sequencing (scRNA-seq) of 19 paired-blood samples revealed an increased proportion of certain NK cell subsets (c4-ZEB2, c5-IL7, c6-IL15, c10-NCR3, and c11-TNFSF8) and T-cell subsets (CD8+ Teff and CD4+ Tem) in responders, characterized by increased expression of proinflammatory and effector molecules. Bulk T-cell receptor (TCR) Vβ repertoire sequencing of responders indicated potential T-cell clonal expansion, manifested as a greater abundance of large and hyperexpanded clonotypes. Our first-in-human trial demonstrated its safety and potentially preliminary efficacy, warranting further clinical evaluation. Multiomic profiling identified specific circulating NK and T-cell subsets potentially associated with clinical outcomes, providing novel insights into the dynamic transcriptional underpinnings of the immune landscape in response to NK cell-based therapy.
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TREM2 is associated with tumor immunity and implies poor prognosis in glioma.
Triggering receptor expressed on myeloid cells 2 (TREM2) is expressed in myeloid cells of the central nervous system (CNS), which mediate the immunological response in a variety of diseases. Uncertain is the function of TREM2 in glioma and tumor immune responses. In this research, the expression patterns of TREM2 in glioma were analyzed, along with its prognostic value and functional roles. TREM2 expression is increased in glioblastomas, gliomas with a mesenchymal subtype, gliomas with wild-type isocitrate dehydrogenase, and gliomas without 1p/19q deletion, all of which suggest the aggressiveness and poor prognosis of gliomas. Gene ontology, KEGG, and Gene set variation analyses indicated that TREM2 may serve as an immune response mediator. However, the function of T cells against tumor cells was negatively correlated with TREM2, suggesting that TREM2 may suppress tumor immunity. Further investigation demonstrated a correlation between TREM2 expression and immune checkpoint expression. CIBERSORT research revealed a link between a higher TREM2 expression level and the enrichment of tumor-associated macrophages, especially M2 subtype. Single-cell analysis and multiple immunohistochemical staining results showed that microglia and macrophage cells expressed TREM2. Immunofluorescent staining indicated that knocking down the expression of TREM2 would result in a decrease in M2 polarization. TREM2 was discovered to be an independent prognostic factor in glioma. In conclusion, our findings revealed that TREM2 was significantly expressed in microglia and macrophage cells and was intimately associated with the tumor immune microenvironment. Thus, it is expected that small-molecule medications targeting TREM2 or monoclonal antibodies would enhance the efficacy of glioma immunotherapy.
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Cross-Talk of Multiple Types of RNA Modification Regulators Uncovers the Tumor Microenvironment and Immune Infiltrates in Soft Tissue Sarcoma.
BACKGROUND: Soft-tissue sarcoma (STS) represents a rare and diverse cohort of solid tumors, and encompasses over 100 various histologic and molecular subtypes. In recent years, RNA modifications including m6A, m5C, m1A, and m7G have been demonstrated to regulate immune response and tumorigenesis. Nevertheless, the cross-talk among these RNA modification regulators and related effects upon the tumor microenvironment (TME), immune infiltrates, and immunotherapy in STS remain poorly understood. METHODS: In this study, we comprehensively investigated transcriptional and genetic alterations of 32 RNA modification regulators in STS patients from The Cancer Genome Atlas (TCGA) cohort and validated them in the Gene Expression Omnibus (GEO) cohort. Single-cell transcriptomes were introduced to identify regulators within specific cell types, with own sequencing data and RT-qPCR conducted for biological validation. Distinct regulator clusters and regulator gene subtypes were identified by using unsupervised consensus clustering analysis. We further built the regulator score model based on the prognostic regulator-related differentially expressed genes (DEGs), which could be used to quantitatively assess the risk for individual STS patients. The clinical and biological characteristics of different regulator score groups were further examined. RESULTS: A total of 455 patients with STS were included in this analysis. The network of 32 RNA modification regulators demonstrated significant correlations within multiple different RNA modification types. Distinct regulator clusters and regulator gene subtypes were characterized by markedly different prognoses and TME landscapes. The low regulator score group in the TCGA-SARC cohort was characterized by poor prognosis. The robustness of the scoring model was further confirmed by the external validation in GSE30929 and GSE17674. The regulator score was negatively correlated with CD4+ T cell, Th2 cell, and Treg cell recruitment and most immunotherapy-predicted pathways, and was also associated with immunotherapy efficacy. CONCLUSIONS: Overall, our study is the first to demonstrate the cross-talk of RNA modification regulators and the potential roles in TME and immune infiltrates in STS. The individualized assessment based on the regulator score model could facilitate and optimize personalized treatment.
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Single-cell spatial transcriptome reveals cell-type organization in the macaque cortex.
Elucidating the cellular organization of the cerebral cortex is critical for understanding brain structure and function. Using large-scale single-nucleus RNA sequencing and spatial transcriptomic analysis of 143 macaque cortical regions, we obtained a comprehensive atlas of 264 transcriptome-defined cortical cell types and mapped their spatial distribution across the entire cortex. We characterized the cortical layer and region preferences of glutamatergic, GABAergic, and non-neuronal cell types, as well as regional differences in cell-type composition and neighborhood complexity. Notably, we discovered a relationship between the regional distribution of various cell types and the region's hierarchical level in the visual and somatosensory systems. Cross-species comparison of transcriptomic data from human, macaque, and mouse cortices further revealed primate-specific cell types that are enriched in layer 4, with their marker genes expressed in a region-dependent manner. Our data provide a cellular and molecular basis for understanding the evolution, development, aging, and pathogenesis of the primate brain.
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Targeting PYCR2 inhibits intraperitoneal metastatic tumors of mouse colorectal cancer in a proline-independent approach.
Whether proline deficiency is a metabolic vulnerability in colorectal tumors is unknown. The aim of this study was to investigate the effects of proline metabolism-related genes and exogenous proline on the progression of colorectal cancer (CRC). We aimed to further clarify the role of pyrroline-5-carboxylate reductase (PYCR) 2, a key enzyme of proline synthesis, in the regulation of colorectal intraperitoneal metastatic tumors. This study was carried out based on The Cancer Genome Atlas (TCGA) data, database analysis, single-cell functional analysis, tissue microarray, cell experiments, and animal models. We found that, PYCR2 mRNA and protein levels were upregulated in CRC. The mRNA level of PYCR2 was closely related to the prognosis and tumor metastasis of CRC patients. The upregulated PYCR2 expression was at least partly due to low promoter methylation levels. The nomogram constructed based on PYCR2 expression and clinical characteristics of CRC showed good accuracy in predicting lymph node metastasis. Pycr2 knockdown inhibited epithelial-mesenchymal transition (EMT) of mouse CRC cells. Proline supplementation did not rescue the inhibition of mouse CRC cell proliferation and migration by Pycr2 knockdown. Proline supplementation also did not rescue the suppression of subcutaneous tumors and intraperitoneal metastatic tumors in mice by Pycr2 knockdown. PYCR2 co-expressed genes in TCGA-CRC were enriched in epigenetic modification-related biological processes and molecular functions. Four small molecules with the lowest binding energy to the PYCR2 protein were identified. Collectively, Pycr2 knockdown inhibited mouse CRC progression in a proline-independent approach. PYCR2 may be a promising tumor metastasis predictor and therapeutic target in CRC.
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Deep learning-based classifier for malignant plasma cell identification in myeloma.
Multiple myeloma (MM) displays significant genetic heterogeneity, making it challenging to distinguish malignant from non-malignant plasma cells in single-cell datasets. Existing marker-based and CNV detection methods require manual intervention or high computational resources, limiting their scope. We developed a supervised deep learning autoencoder to classify malignant cells across MM and its precursor stages and validated its performance and biological relevance. The model outperformed alternative approaches, achieving mean AUCs of 0.86 on internal and 0.80 on external datasets, with strong performance on unseen samples across single-cell platforms (mean AUC 0.92). Despite training solely on MM samples, our model distinguishes malignant cells in precursor stages, with predicted malignant cell proportions increasing from monoclonal gammopathy of undetermined significance (MGUS, 29.00%) to smoldering multiple myeloma (SMM, 77.83%) and MM (96.08%). In paired samples, the model also accurately captured a reduction in malignant plasma cell proportions following treatment, reflecting patient variability in treatment response. Differential expression analysis uncovered a 12-gene malignant plasma signature linked to poor survival (HR = 2.7, Log-rank P = 0.0023) and validated in the MMRF CoMMpass cohort. Overall, our resulting model enables accurate malignant cell identification and provides biologically and clinically relevant insights for MM research.