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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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Multi-Omics Analysis Identifying Key Biomarkers in Ovarian Cancer.
Ovarian cancer is one of the most common malignant tumors. Here, we aimed to study the expression and function of the CREB1 gene in ovarian cancer via the bioinformatic analyses of multiple databases. Previously, the prognosis of ovarian cancer was based on single-factor or single-gene studies. In this study, different bioinformatics tools (such as TCGA, GEPIA, UALCAN, MEXPRESS, and Metascape) have been used to assess the expression and prognostic value of the CREB1 gene. We used the Reactome and cBioPortal databases to identify and analyze CREB1 mutations, copy number changes, expression changes, and protein-protein interactions. By analyzing data on the CREB1 differential expression in ovarian cancer tissues and normal tissues from 12 studies collected from the "Human Protein Atlas" database, we found a significantly higher expression of CREB1 in normal ovarian tissues. Using this database, we collected information on the expression of 25 different CREB-related proteins, including TP53, AKT1, and AKT3. The enrichment of these factors depended on tumor metabolism, invasion, proliferation, and survival. Individualized tumors based on gene therapy related to prognosis have become a new possibility. In summary, we established a new type of prognostic gene profile for ovarian cancer using the tools of bioinformatics.
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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.
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Integrated histopathology, spatial and single cell transcriptomics resolve cellular drivers of early and late alveolar damage in COVID-19.
The most common cause of death due to COVID-19 remains respiratory failure. Yet, our understanding of the precise cellular and molecular changes underlying lung alveolar damage is limited. Here, we integrate single cell transcriptomic data of COVID-19 and donor lung tissue with spatial transcriptomic data stratifying histopathological stages of diffuse alveolar damage. We identify changes in cellular composition across progressive damage, including waves of molecularly distinct macrophages and depletion of epithelial and endothelial populations. Predicted markers of pathological states identify immunoregulatory signatures, including IFN-alpha and metallothionein signatures in early damage, and fibrosis-related collagens in late damage. Furthermore, we predict a fibrinolytic shutdown via endothelial upregulation of SERPINE1/PAI-1. Cell-cell interaction analysis revealed macrophage-derived SPP1/osteopontin signalling as a key regulator during early steps of alveolar damage. These results provide a comprehensive, spatially resolved atlas of alveolar damage progression in COVID-19, highlighting the cellular mechanisms underlying pro-inflammatory and pro-fibrotic pathways in severe disease.
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Identification and validation of neurotrophic factor-related gene signatures in glioblastoma and Parkinson's disease.
BACKGROUND: Glioblastoma multiforme (GBM) is the most common cancer of the central nervous system, while Parkinson's disease (PD) is a degenerative neurological condition frequently affecting the elderly. Neurotrophic factors are key factors associated with the progression of degenerative neuropathies and gliomas. METHODS: The 2601 neurotrophic factor-related genes (NFRGs) available in the Genecards portal were analyzed and 12 NFRGs with potential roles in the pathogenesis of Parkinson's disease and the prognosis of GBM were identified. LASSO regression and random forest algorithms were then used to screen the key NFRGs. The correlation of the key NFRGs with immune pathways was verified using GSEA (Gene Set Enrichment Analysis). A prognostic risk scoring system was constructed using LASSO (Least absolute shrinkage and selection operator) and multivariate Cox risk regression based on the expression of the 12 NFRGs in the GBM cohort from The Cancer Genome Atlas (TCGA) database. We also investigated differences in clinical characteristics, mutational landscape, immune cell infiltration, and predicted efficacy of immunotherapy between risk groups. Finally, the accuracy of the model genes was validated using multi-omics mutation analysis, single-cell sequencing, QT-PCR, and HPA. RESULTS: We found that 4 NFRGs were more reliable for the diagnosis of Parkinson's disease through the use of machine learning techniques. These results were validated using two external cohorts. We also identified 7 NFRGs that were highly associated with the prognosis and diagnosis of GBM. Patients in the low-risk group had a greater overall survival (OS) than those in the high-risk group. The nomogram generated based on clinical characteristics and risk scores showed strong prognostic prediction ability. The NFRG signature was an independent prognostic predictor for GBM. The low-risk group was more likely to benefit from immunotherapy based on the degree of immune cell infiltration, expression of immune checkpoints (ICs), and predicted response to immunotherapy. In the end, 2 NFRGs (EN1 and LOXL1) were identified as crucial for the development of Parkinson's disease and the outcome of GBM. CONCLUSIONS: Our study revealed that 4 NFRGs are involved in the progression of PD. The 7-NFRGs risk score model can predict the prognosis of GBM patients and help clinicians to classify the GBM patients into high and low risk groups. EN1, and LOXL1 can be used as therapeutic targets for personalized immunotherapy for patients with PD and GBM.
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Single-cell transcriptomics reveals macrophage-neutrophil crosstalk in Haematitum-induced lung injury associated with gut dysbiosis.
BACKGROUND: Haematitum is a mineral-based traditional Chinese medicine widely used in clinical applications. Both the Chinese Materia Medica and modern research indicate that it exhibits lung toxicity and should not be administered over prolonged periods. The precise mechanism underlying this toxicity, however, remains incompletely understood. PURPOSE: This study aims to systematically elucidate the toxic mechanism of lung injury induced by Haematitum administration, emphasizing the central role of the gut-lung axis in this process. METHODS: The mouse model of lung injury induced by Haematitum was established. A combination of 16S rRNA sequencing, untargeted metabolomics, and single-cell RNA sequencing was utilized to comprehensively analyze alterations in the intestinal microbiota, lung tissue metabolism, and lung cell atlas. The pharmacological validation was conducted by administering PPARγ agonists and TNF-α inhibitors. Furthermore, the detoxification effect of its classical combination with Inula japonica Thunb. was also evaluated. RESULTS: High-dose Haematitum disrupted the intestinal barrier and induced dysbiosis. Metabolites from the gut microbiota entered the bloodstream and exerted a pathogenic effect. Correlation analysis showed that gut microbiota dysbiosis is strongly associated with the pulmonary glycerophospholipid metabolic pathway. Results from single-cell sequencing and macrophage/TNF-α colocalization experiments indicated that the macrophages' PPAR signaling pathway was inhibited, inactivating their anti-inflammatory function and triggering the release of inflammatory factors such as TNF-α. Acting as a key messenger, TNF-α activated the TNF signaling pathway in neutrophils, driving them to acquire a highly destructive phenotype. The synergy between macrophages and neutrophils ultimately provoked inflammation and cell apoptosis in lung tissue. Pharmacological intervention of the PPARγ/TNF-α axis can effectively reverse cellular abnormalities and significantly alleviate lung injury. The combination with Inula japonica Thunb. also reduced lung toxicity, and components within this herb are predicted to act as natural PPARγ agonists. CONCLUSIONS: This study has revealed a potential toxic mechanism. The intestinal barrier in mice was compromised, with an increase in the relative abundance of Klebsiella, entry of microbial metabolites into the bloodstream, and disruption of glycerophospholipid metabolism in the lungs, which in turn resulted in dysfunction of pulmonary immune cells. It clarifies that the core mechanism of Haematitum-induced lung injury lies in inhibiting the PPAR signaling pathway in macrophages and activating the TNF signaling pathway in neutrophils. Administering Haematitum at a common dose or in combination with Inula japonica Thunb. mitigates these safety risks. These findings advance understanding of the toxicology of mineral-based medicines and provide a reference for the safe clinical application of these traditional Chinese medicines.
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USP15 mutation associated with autism spectrum disorder alters progenitor fate and neuronal maturation in human brain organoids.
De novo variants in the ubiquitin-proteasome pathway are linked to autism spectrum disorder (ASD), yet their functional impact on neurodevelopment remains poorly understood. We investigated USP15, a deubiquitinating enzyme with rare damaging variants identified in individuals with ASD, using isogenic human iPSC-derived brain organoids and single-cell transcriptomics. USP15 mutant organoids showed genotype-dependent, progenitor-centered alterations during corticogenesis. Heterozygous organoids modeling haploinsufficiency displayed a shift toward later pseudotime states together with altered maturation and synaptic organization of deep-layer neurons. In contrast, homozygous organoids showed broader phenotypes, including mitotic suppression, aberrant HOX gene expression, and stress-response activation. Regulon analysis showed reduced activity of progenitor-associated regulons, including SOX2, NR2F1, and NR2F2, in heterozygous organoids, whereas homozygous organoids exhibited broader changes in transcriptional regulatory networks. Furthermore, USP15 mutant-associated gene expression patterns were significantly enriched for established ASD risk genes. Comparison with the mouse brain perturbation atlas showed that the transcriptional signature of the USP15 mutant showed notable overlap with those of Fezf2 and Foxp1 mutants, key regulators of deep-layer projection neuron identity. These findings characterize genotype-dependent neurodevelopmental phenotypes associated with reduced USP15 dosage and provide a human neural framework for investigating ASD-relevant developmental mechanisms in the context of a rare ubiquitin-pathway variant.
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A low level of tumor necrosis factor α in tumor microenvironment maintains the self-renewal of glioma stem cells by Vasorin-mediated glycolysis.
BACKGROUND: Self-renewal of glioma stem cells (GSCs) is responsible for glioblastoma (GBM) therapy resistance and recurrence. Tumor necrosis factor α (TNFα) and TNF signaling pathway display an antitumor activity in preclinical models and in tumor patients. However, TNFα exhibits no significance for glioma clinical prognosis based on the Glioma Genome Atlas database. This study aimed to explore whether TNFα of tumor microenvironment maintains self-renewal of GSCs and promotes worse prognosis in glioma patients. METHODS: Spatial transcriptomics, immunoblotting, sphere formation assay, extreme limiting dilution, and gene expression analysis were used to determine the role of TNFα on GSC's self-renewal. Mass spectrometry, RNA-sequencing detection, bioinformatic analyses, qRT-RNA, immunofluorescence, immunohistochemistry, single-cell RNA sequencing, in vitro and in vivo models were used to uncover the mechanism of TNFα-induced GSC self-renewal. RESULTS: A low level of TNFα displays a promoting effect on GSC self-renewal and worse glioma prognosis. Mechanistically, Vasorin (VASN) mediated TNFα-induced self-renewal by potentiating glycolysis. Lactate produced by glycolysis inhibits the TNFα secretion of tumor-associated macrophages (TAMs) and maintains TNFα at a low level. CONCLUSIONS: TNFα-induced GSC self-renewal mediated by VASN provides a possible explanation for the failures of endogenous TNFα effect on GBM. A combination of targeting VASN and TNFα antitumor effect may be an effective approach for treating GBM.
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A computational pipeline for identifying gene targets and signalling pathways in cancer cells to improve lymphocyte infiltration and immune checkpoint therapy efficacy.
BACKGROUND: Tumour-infiltrating lymphocytes (TILs) are crucial for effective immune checkpoint blockade (ICB) therapy in solid tumours. However, ∼70% of these tumours exhibit poor lymphocyte infiltration, rendering ICB therapies less effective. METHODS: We developed a bioinformatics pipeline integrating multiple previously unconsidered factors or datasets, including tumour cell immune-related pathways, copy number variation (CNV), and single tumour cell sequencing data, as well as tumour mRNA-seq data and patient survival data, to identify targets that can potentially improve T cell infiltration and enhance ICB efficacy. Furthermore, we conducted wet-lab experiments and successfully validated one of the top-identified genes. FINDINGS: We applied this pipeline in solid tumours of the Cancer Genome Atlas (TCGA) and identified a set of genes in 18 cancer types that might potentially improve lymphocyte infiltration and ICB efficacy, providing a valuable drug target resource to be further explored. Importantly, we experimentally validated SUN1, which had not been linked to T cell infiltration and ICB therapy previously, but was one of the top-identified gene targets among 3 cancer types based on the pipeline, in a mouse colon cancer syngeneic model. We showed that Sun1 KO could significantly enhance antigen presentation, increase T-cell infiltration, and improve anti-PD1 treatment efficacy. Moreover, with a single-cell multiome analysis, we identified subgene regulatory networks (sub-GRNs) showing Stat proteins play important roles in enhancing the immune-related pathways in Sun1-KO cancer cells. INTERPRETATION: This study not only established a computational pipeline for discovering new gene targets and signalling pathways in cancer cells that block T-cell infiltration, but also provided a gene target pool for further exploration in improving lymphocyte infiltration and ICB efficacy in solid tumours. FUNDING: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.